From eb79753169e0b8be8ee958cbf19a0dac2763b609 Mon Sep 17 00:00:00 2001 From: YoungFish Date: Wed, 13 Dec 2023 23:14:58 +0800 Subject: [PATCH] add MM, CCS, EMNLP 2023 papers and update VLDB, TC, TCAD papers --- README.md | 65 +++++++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 51 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 6b1bf3e..86dab6b 100644 --- a/README.md +++ b/README.md @@ -81,22 +81,22 @@ We use another project to automatically track updates to FL papers, click on [FL | [KDD](https://dblp.uni-trier.de/search?q=federate%20venue%3AKDD%3A) | [23](https://dl.acm.org/doi/proceedings/10.1145/3580305), [22](https://kdd.org/kdd2022/paperRT.html), [21](https://kdd.org/kdd2021/accepted-papers/index), [20](https://www.kdd.org/kdd2020/accepted-papers) | | | [WSDM](https://dblp.uni-trier.de/search?q=federate%20venue%3AWSDM%3A) | [23](https://www.wsdm-conference.org/2023/program/accepted-papers), [22](https://www.wsdm-conference.org/2022/accepted-papers/), [21](https://www.wsdm-conference.org/2021/accepted-papers.php) | [19](https://www.wsdm-conference.org/2019/accepted-papers.php) | | [S&P](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fsp%3A) | [23](https://sp2023.ieee-security.org/program-papers.html), [22](https://www.ieee-security.org/TC/SP2022/program-papers.html) | [19](https://www.ieee-security.org/TC/SP2019/program-papers.html) | -| [CCS](https://dblp.uni-trier.de/search?q=federate%20venue%3ACCS%3A) | [23](https://www.sigsac.org/ccs/CCS2023/program.html#acceptPaperTabContent), [22](https://www.sigsac.org/ccs/CCS2022/program/accepted-papers.html), [21](https://sigsac.org/ccs/CCS2021/accepted-papers.html), [19](https://www.sigsac.org/ccs/CCS2019/index.php/program/accepted-papers/) | [17](https://acmccs.github.io/papers/) | +| [CCS](https://dblp.uni-trier.de/search?q=federate%20venue%3ACCS%3A) | [23](https://dl.acm.org/doi/proceedings/10.1145/3576915), [22](https://www.sigsac.org/ccs/CCS2022/program/accepted-papers.html), [21](https://sigsac.org/ccs/CCS2021/accepted-papers.html), [19](https://www.sigsac.org/ccs/CCS2019/index.php/program/accepted-papers/) | [17](https://acmccs.github.io/papers/) | | [USENIX Security](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fuss%3A) | [23](https://www.usenix.org/conference/usenixsecurity23/technical-sessions), [22](https://www.usenix.org/conference/usenixsecurity22/technical-sessions), [20](https://www.usenix.org/conference/usenixsecurity20/technical-sessions) | - | | [NDSS](https://dblp.uni-trier.de/search?q=federate%20venue%3ANDSS%3A) | [23](https://www.ndss-symposium.org/ndss2023/accepted-papers/), [22](https://www.ndss-symposium.org/ndss2022/accepted-papers/), [21](https://www.ndss-symposium.org/ndss2021/accepted-papers/) | - | | [CVPR](https://dblp.uni-trier.de/search?q=federate%20venue%3ACVPR%3A) | [23](https://openaccess.thecvf.com/CVPR2023?day=all), [22](https://openaccess.thecvf.com/CVPR2022), [21](https://openaccess.thecvf.com/CVPR2021?day=all) | - | | [ICCV](https://dblp.uni-trier.de/search?q=federate%20venue%3AICCV%3A) | [23](https://openaccess.thecvf.com/ICCV2023?day=all),[21](https://openaccess.thecvf.com/ICCV2021?day=all) | - | | [ECCV](https://dblp.uni-trier.de/search?q=federate%20venue%3AECCV%3A) | [22](https://www.ecva.net/papers.php), [20](https://www.ecva.net/papers.php) | - | -| [MM](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fmm%3A) | [22](https://dblp.uni-trier.de/db/conf/mm/mm2022.html), [21](https://2021.acmmm.org/main-track-list), [20](https://2020.acmmm.org/main-track-list.html) | - | +| [MM](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fmm%3A) | [23](https://dl.acm.org/doi/proceedings/10.1145/3581783), [22](https://dblp.uni-trier.de/db/conf/mm/mm2022.html), [21](https://2021.acmmm.org/main-track-list), [20](https://2020.acmmm.org/main-track-list.html) | - | | [IJCV](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Fijcv%3A) (J) | - | - | | [ACL](https://dblp.uni-trier.de/search?q=federate%20venue%3AACL%3A) | [23](https://aclanthology.org/events/acl-2023/), [22](https://aclanthology.org/events/acl-2022/), [21](https://aclanthology.org/events/acl-2021/) | [19](https://aclanthology.org/events/acl-2019/) | | [NAACL](https://dblp.uni-trier.de/search?q=federate%20venue%3ANAACL-HLT%3A) | [22](https://aclanthology.org/events/naacl-2022/), [21](https://aclanthology.org/events/naacl-2021/) | - | -| [EMNLP](https://dblp.uni-trier.de/search?q=federate%20venue%3AEMNLP%3A) | [22](https://aclanthology.org/events/emnlp-2022/), [21](https://aclanthology.org/events/emnlp-2021/), [20](https://aclanthology.org/events/emnlp-2020/) | - | +| [EMNLP](https://dblp.uni-trier.de/search?q=federate%20venue%3AEMNLP%3A) | [23](https://aclanthology.org/events/emnlp-2023/), [22](https://aclanthology.org/events/emnlp-2022/), [21](https://aclanthology.org/events/emnlp-2021/), [20](https://aclanthology.org/events/emnlp-2020/) | - | | [COLING](https://dblp.uni-trier.de/search?q=federate%20venue%3ACOLING%3A) | [20](https://aclanthology.org/events/coling-2020/) | - | | [SIGIR](https://dblp.uni-trier.de/search?q=federate%20venue%3ASIGIR%3A) | [23](https://dl.acm.org/doi/proceedings/10.1145/3539618), [22](https://dl.acm.org/doi/proceedings/10.1145/3477495), [21](https://dl.acm.org/doi/proceedings/10.1145/3404835), [20](https://dl.acm.org/doi/proceedings/10.1145/3397271) | - | | [SIGMOD](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fsigmod%3A) | [22](https://2022.sigmod.org/sigmod_research_list.shtml), [21](https://2021.sigmod.org/sigmod_research_list.shtml) | - | | [ICDE](https://dblp.uni-trier.de/search?q=federate%20venue%3AICDE%3A) | [23](https://icde2023.ics.uci.edu/papers-research-track/), [22](https://icde2022.ieeecomputer.my/accepted-research-track/), [21](https://ieeexplore.ieee.org/xpl/conhome/9458599/proceeding) | - | -| [VLDB](https://dblp.org/search?q=federated%20streamid%3Ajournals%2Fpvldb%3A) | 23, [22](https://vldb.org/pvldb/vol16-volume-info/), [21](https://vldb.org/pvldb/vol15-volume-info/), [21](http://www.vldb.org/pvldb/vol14/), [20](http://vldb.org/pvldb/vol13-volume-info/) | - | +| [VLDB](https://dblp.org/search?q=federated%20streamid%3Ajournals%2Fpvldb%3A) | [23](https://vldb.org/pvldb/volumes/17), [22](https://vldb.org/pvldb/vol16-volume-info/), [21](https://vldb.org/pvldb/vol15-volume-info/), [21](http://www.vldb.org/pvldb/vol14/), [20](http://vldb.org/pvldb/vol13-volume-info/) | - | | [SIGCOMM](https://dblp.uni-trier.de/search?q=federate%20venue%3ASIGCOMM%3A) | - | - | | [INFOCOM](https://dblp.uni-trier.de/search?q=federate%20venue%3AINFOCOM%3A) | [23](https://infocom2023.ieee-infocom.org/program/accepted-paper-list-main-conference), [22](https://infocom2022.ieee-infocom.org/program/accepted-paper-list-main-conference), [21](https://infocom2021.ieee-infocom.org/accepted-paper-list-main-conference.html), [20](https://infocom2020.ieee-infocom.org/accepted-paper-list-main-conference.html) | [19](https://infocom2019.ieee-infocom.org/accepted-paper-list-main-conference.html), 18 | | [MobiCom](https://dblp.uni-trier.de/search?q=federate%20venue%3AMobiCom%3A) | [23](https://www.sigmobile.org/mobicom/2023/accepted.html), [22](https://www.sigmobile.org/mobicom/2022/accepted.html), [21](https://www.sigmobile.org/mobicom/2021/accepted.html), [20](https://www.sigmobile.org/mobicom/2020/accepted.php) | | @@ -438,6 +438,8 @@ Federated Learning papers accepted by top ML(machine learning) conference and jo | A First Look into the Carbon Footprint of Federated Learning | University of Cambridge | JMLR | 2023 | [[PUB](https://jmlr.org/papers/v24/21-0445.html)] [[PDF](https://arxiv.org/abs/2102.07627)] | | Attacks against Federated Learning Defense Systems and their Mitigation | The University of Newcastle | JMLR | 2023 | [[PUB](https://jmlr.org/papers/v24/22-0014.html)] [[CODE](https://github.com/codymlewis/viceroy)] | | A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates | Universit ́e Cˆ ote d’Azur | JMLR | 2023 | [[PUB](https://jmlr.org/papers/v24/22-0689.html)] [[PDF](https://arxiv.org/abs/2206.10189)] [[CODE](https://github.com/Accenture/Labs-Federated-Learning/tree/asynchronous_FL)] | +| Tighter Regret Analysis and Optimization of Online Federated Learning | Hanyang University | TPAMI | 2023 | [PUB](https://ieeexplore.ieee.org/document/10255290) [PDF](https://arxiv.org/abs/2205.06491) | +| Efficient Federated Learning Via Local Adaptive Amended Optimizer With Linear Speedup | University of Sydney | TPAMI | 2023 | PUB [PDF](https://arxiv.org/abs/2308.00522) | | Federated Learning Via Inexact ADMM. | BJTU | TPAMI | 2023 | [[PUB](https://ieeexplore.ieee.org/document/10040221)] [[PDF](https://arxiv.org/abs/2204.10607)] [[CODE](https://github.com/ShenglongZhou/FedADMM)] | | FedIPR: Ownership Verification for Federated Deep Neural Network Models | SJTU | TPAMI | 2023 | [[PUB](https://ieeexplore.ieee.org/document/9847383)] [[PDF](https://arxiv.org/abs/2109.13236)] [[CODE](https://github.com/purp1eHaze/FedIPR)] [[解读](https://zhuanlan.zhihu.com/p/562837170)] | | Decentralized Federated Averaging | NUDT | TPAMI | 2023 | [[PUB](https://ieeexplore.ieee.org/document/9850408)] [[PDF](https://arxiv.org/abs/2104.11375)] | @@ -813,7 +815,7 @@ Federated Learning papers accepted by top DM(Data Mining) conference and journal Federated Learning papers accepted by top Secure conference and journal, Including [S&P](https://dblp.uni-trier.de/db/conf/sp/index.html)(IEEE Symposium on Security and Privacy), [CCS](https://dblp.uni-trier.de/db/conf/ccs/index.html)(Conference on Computer and Communications Security), [USENIX Security](https://dblp.uni-trier.de/db/conf/uss/index.html)(Usenix Security Symposium) and [NDSS](https://dblp.uni-trier.de/db/conf/ndss/index.html)(Network and Distributed System Security Symposium). - [S&P](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fsp%3A) [2023](https://sp2023.ieee-security.org/program-papers.html), [2022](https://www.ieee-security.org/TC/SP2022/program-papers.html), [2019](https://www.ieee-security.org/TC/SP2019/program-papers.html) -- [CCS](https://dblp.uni-trier.de/search?q=federate%20venue%3ACCS%3A) [2022](https://www.sigsac.org/ccs/CCS2022/program/accepted-papers.html), [2021](https://sigsac.org/ccs/CCS2021/accepted-papers.html), [2019](https://www.sigsac.org/ccs/CCS2019/index.php/program/accepted-papers/), [2017](https://acmccs.github.io/papers/) +- [CCS](https://dblp.uni-trier.de/search?q=federate%20venue%3ACCS%3A) [2023](https://dl.acm.org/doi/proceedings/10.1145/3576915), [2022](https://www.sigsac.org/ccs/CCS2022/program/accepted-papers.html), [2021](https://sigsac.org/ccs/CCS2021/accepted-papers.html), [2019](https://www.sigsac.org/ccs/CCS2019/index.php/program/accepted-papers/), [2017](https://acmccs.github.io/papers/) - [USENIX Security](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fuss%3A) [2023](https://www.usenix.org/conference/usenixsecurity23/technical-sessions), [2022](https://www.usenix.org/conference/usenixsecurity22/technical-sessions), [2020](https://www.usenix.org/conference/usenixsecurity20/technical-sessions) - [NDSS](https://dblp.uni-trier.de/search?q=federate%20venue%3ANDSS%3A) [2023](https://www.ndss-symposium.org/ndss2023/accepted-papers/), [2022](https://www.ndss-symposium.org/ndss2022/accepted-papers/), [2021](https://www.ndss-symposium.org/ndss2021/accepted-papers/) @@ -823,6 +825,12 @@ Federated Learning papers accepted by top Secure conference and journal, Includi |Title | Affiliation | Venue | Year | Materials| | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- | ---- | ------------------------------------------------------------ | +|Turning Privacy-preserving Mechanisms against Federated Learning | University of Pavia | CCS | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3576915.3623114) [PDF](https://arxiv.org/abs/2305.05355) | +|MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers | University of Würzburg | CCS | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3576915.3623212) | +|martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture | THU | CCS | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3576915.3623134) [PDF](https://arxiv.org/abs/2309.01098) [CODE](https://github.com/liqi16/martfl) | +|Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks | UIUC | CCS | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3576915.3623193) [PDF](https://arxiv.org/abs/2209.04030) | +|Poster: Verifiable Data Valuation with Strong Fairness in Horizontal Federated Learning | NSYSU | CCS | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3576915.3624371) | +|Poster: Bridging Trust Gaps: Data Usage Transparency in Federated Data Ecosystems | RWTH Aachen University | CCS | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3576915.3624371) | | Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks | University of Massachusetts Amherst | USENIX Security | 2023 | [[PUB](https://www.usenix.org/conference/usenixsecurity23/presentation/mozaffari)] [[PDF](https://arxiv.org/abs/2110.04350)] | | PrivateFL: Accurate, Differentially Private Federated Learning via Personalized Data Transformation | JHU | USENIX Security | 2023 | [[PUB](https://www.usenix.org/conference/usenixsecurity23/presentation/yang-yuchen)] [[CODE](https://github.com/BHui97/PrivateFL)] | | Gradient Obfuscation Gives a False Sense of Security in Federated Learning | NCSU | USENIX Security | 2023 | [[PUB](https://www.usenix.org/conference/usenixsecurity23/presentation/yue)] [[PDF](https://arxiv.org/abs/2206.04055)] [[CODE](https://github.com/KAI-YUE/rog)] | @@ -876,7 +884,7 @@ Federated Learning papers accepted by top CV(computer vision) conference and jou - [CVPR](https://dblp.uni-trier.de/search?q=federate%20venue%3ACVPR%3A) [2023](https://openaccess.thecvf.com/CVPR2023?day=all), [2022](https://openaccess.thecvf.com/CVPR2022), [2021](https://openaccess.thecvf.com/CVPR2021?day=all) - [ICCV](https://dblp.uni-trier.de/search?q=federate%20venue%3AICCV%3A) [2023](https://openaccess.thecvf.com/ICCV2023?day=all), [2021](https://openaccess.thecvf.com/ICCV2021?day=all) - [ECCV](https://dblp.uni-trier.de/search?q=federate%20venue%3AECCV%3A) [2022](https://www.ecva.net/papers.php), [2020](https://www.ecva.net/papers.php) -- [MM](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fmm%3A) [2022](https://dblp.uni-trier.de/db/conf/mm/mm2022.html), [2021](https://2021.acmmm.org/main-track-list), [2020](https://2020.acmmm.org/main-track-list.html) +- [MM](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fmm%3A) [2023](https://dl.acm.org/doi/proceedings/10.1145/3581783), [2022](https://dblp.uni-trier.de/db/conf/mm/mm2022.html), [2021](https://2021.acmmm.org/main-track-list), [2020](https://2020.acmmm.org/main-track-list.html) - [IJCV](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Fijcv%3A) NULL
@@ -886,6 +894,22 @@ Federated Learning papers accepted by top CV(computer vision) conference and jou |Title | Affiliation | Venue | Year | Materials| | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- | ---- | ------------------------------------------------------------ | +|FedCE: Personalized Federated Learning Method based on Clustering Ensembles | BJTU | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3612217) | +|FedVQA: Personalized Federated Visual Question Answering over Heterogeneous Scenes | Leiden University | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3611958) | +|Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor | XJTU | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3612597) [PDF](https://arxiv.org/abs/2312.02416) [CODE](https://github.com/J1nqianChen/FedKA) | +|Federated Deep Multi-View Clustering with Global Self-Supervision | UESTC | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3612027) [PDF](https://arxiv.org/abs/2309.13697) | +|FedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy | ZJU | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3611953) | +|Prototype-guided Knowledge Transfer for Federated Unsupervised Cross-modal Hashing | SDNU | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3613837) [CODE](https://github.com/exquisite1210/PT-FUCH_P) | +|Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data | ZJU | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3612178) [PDF](https://arxiv.org/abs/2308.11646) | +|FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data | BUPT | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3611966) | +|Federated Learning with Label-Masking Distillation | UCAS | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3611984) [CODE](https://github.com/wnma3mz/FedLMD) | +|Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data | SDU | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3612481) [PDF](https://arxiv.org/abs/2308.03457) [CODE](https://github.com/qizhuang-qz/FedCSPC) | +|A Four-Pronged Defense Against Byzantine Attacks in Federated Learning | HUST | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3612474) [PDF](https://arxiv.org/abs/2308.03331) | +|Client-Adaptive Cross-Model Reconstruction Network for Modality-Incomplete Multimodal Federated Learning | CAS; Peng Cheng Laboratory; UCAS | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3611757) | +|FedGH: Heterogeneous Federated Learning with Generalized Global Header | NKU | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3611781) [PDF](https://arxiv.org/abs/2303.13137) [CODE](https://github.com/LipingYi/FedGH) | +|Cuing Without Sharing: A Federated Cued Speech Recognition Framework via Mutual Knowledge Distillation | CUHK | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3612134) [PDF](https://arxiv.org/abs/2308.03432) [CODE](https://github.com/yuxuanzhang0713/fedcsr) | +|AffectFAL: Federated Active Affective Computing with Non-IID Data | TJUT | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3612442) [CODE](https://github.com/AffectFAL/AffectFAL) | +|Improving Federated Person Re-Identification through Feature-Aware Proximity and Aggregation | SZU | MM | 2023 | [PUB](https://dl.acm.org/doi/10.1145/3581783.3612350) | | Towards Attack-tolerant Federated Learning via Critical Parameter Analysis | KAIST | ICCV | 2023 | [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Han_Towards_Attack-tolerant_Federated_Learning_via_Critical_Parameter_Analysis_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2308.09318)] [[CODE](https://github.com/Sungwon-Han/FEDCPA)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Han_Towards_Attack-tolerant_Federated_ICCV_2023_supplemental.pdf)] | | Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation | NTU; NVIDIA | ICCV | 2023 | [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Yang_Efficient_Model_Personalization_in_Federated_Learning_via_Client-Specific_Prompt_Generation_ICCV_2023_paper.html)] [[PDF](https://arxiv.org/abs/2308.15367)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Yang_Efficient_Model_Personalization_ICCV_2023_supplemental.pdf)] | | Generative Gradient Inversion via Over-Parameterized Networks in Federated Learning | A*STAR | ICCV | 2023 | [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Zhang_Generative_Gradient_Inversion_via_Over-Parameterized_Networks_in_Federated_Learning_ICCV_2023_paper.html)] [[CODE](https://github.com/czhang024/CI-Net)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Zhang_Generative_Gradient_Inversion_ICCV_2023_supplemental.pdf)] | @@ -1004,7 +1028,7 @@ Federated Learning papers accepted by top AI and NLP conference and journal, inc - [ACL](https://dblp.uni-trier.de/search?q=federate%20venue%3AACL%3A) [2023](https://aclanthology.org/events/acl-2023/), [2022](https://aclanthology.org/events/acl-2022/), [2021](https://aclanthology.org/events/acl-2021/), [2019](https://aclanthology.org/events/acl-2019/) - [NAACL](https://dblp.uni-trier.de/search?q=federate%20venue%3ANAACL-HLT%3A) [2022](https://aclanthology.org/events/naacl-2022/), [2021](https://aclanthology.org/events/naacl-2021/) -- [EMNLP](https://dblp.uni-trier.de/search?q=federate%20venue%3AEMNLP%3A) [2022](https://aclanthology.org/events/emnlp-2022/), [2021](https://aclanthology.org/events/emnlp-2021/), [2020](https://aclanthology.org/events/emnlp-2020/) +- [EMNLP](https://dblp.uni-trier.de/search?q=federate%20venue%3AEMNLP%3A) [2023](https://aclanthology.org/events/emnlp-2023/), [2022](https://aclanthology.org/events/emnlp-2022/), [2021](https://aclanthology.org/events/emnlp-2021/), [2020](https://aclanthology.org/events/emnlp-2020/) - [COLING](https://dblp.uni-trier.de/search?q=federate%20venue%3ACOLING%3A) [2020](https://aclanthology.org/events/coling-2020/)
@@ -1013,6 +1037,12 @@ Federated Learning papers accepted by top AI and NLP conference and journal, inc |Title | Affiliation | Venue | Year | Materials| | ------------------------------------------------------------ | ------------------------------------------------- | -------------- | ---- | ------------------------------------------------------------ | +|Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization | Auburn University | EMNLP | 2023 | [PUB](https://aclanthology.org/2023.emnlp-main.488/) [PDF](https://arxiv.org/abs/2310.15080) [CODE](https://github.com/llm-eff/FedPepTAO) | +| Federated Meta-Learning for Emotion and Sentiment Aware Multi-modal Complaint Identification | IIT Patna | EMNLP | 2023 | [[PUB](https://aclanthology.org/2023.emnlp-main.999/)] [CODE](https://github.com/appy1608/EMNLP2023-Multimodal-Complaint-Detection) | +|FedID: Federated Interactive Distillation for Large-Scale Pretraining Language Models | YNU | EMNLP | 2023 | [PUB](https://aclanthology.org/2023.emnlp-main.529/) [CODE](https://github.com/maxinge8698/FedID) | +|FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning | KAIST | EMNLP | 2023 | [PUB](https://aclanthology.org/2023.emnlp-main.734/) [PDF](https://arxiv.org/abs/2310.16538) | +|Coordinated Replay Sample Selection for Continual Federated Learning | CMU | EMNLP industry Track | 2023 | [PUB](https://aclanthology.org/2023.emnlp-industry.32/) [PDF](https://arxiv.org/abs/2310.15054) | +|Tunable Soft Prompts are Messengers in Federated Learning | SYSU | EMNLP Findings | 2023 | [PUB](https://aclanthology.org/2023.findings-emnlp.976/) [PDF](https://arxiv.org/abs/2311.06805) [CODE](https://github.com/alibaba/FederatedScope/tree/fedsp/federatedscope/nlp/fedsp) | | Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms | OSU | ACL | 2023 | [[PUB](https://aclanthology.org/2023.acl-long.678/)] [[PDF](https://arxiv.org/abs/2305.17221)] [[CODE](https://github.com/osu-nlp-group/fl4semanticparsing)] | | FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP | HIT; Peng Cheng Lab | ACL | 2023 | [[PUB](https://aclanthology.org/2023.acl-long.193/)] [[CODE](https://github.com/SMILELab-FL/FedLegal)] | | Client-Customized Adaptation for Parameter-Efficient Federated Learning | | ACL Findings | 2023 | [[PUB](https://aclanthology.org/2023.findings-acl.75/)] | @@ -1020,14 +1050,13 @@ Federated Learning papers accepted by top AI and NLP conference and journal, inc | Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets | | ACL Findings | 2023 | [[PUB](https://aclanthology.org/2023.findings-acl.470/)] | | FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models | | ACL Findings | 2023 | [[PUB](https://aclanthology.org/2023.findings-acl.632/)] | | Federated Learning of Gboard Language Models with Differential Privacy | | ACL Industry Track | 2023 | [[PUB](https://aclanthology.org/2023.acl-industry.60/)] [[PDF](https://arxiv.org/abs/2305.18465)] | -| Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation | PKU | EMNLP | 2022 | [[PUB](https://aclanthology.org/2022.findings-emnlp.25/)] [[PDF](https://arxiv.org/abs/2210.06894)] | -| Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation **`kg.`** | Lehigh University | EMNLP | 2022 | [[PUB](https://aclanthology.org/2022.findings-emnlp.43/)] [[PDF](https://arxiv.org/abs/2203.09553)] [[CODE](https://github.com/taokz/FedR)] | -| Federated Continual Learning for Text Classification via Selective Inter-client Transfer | DRIMCo GmbH; LMU | EMNLP | 2022 | [[PUB](https://aclanthology.org/2022.findings-emnlp.353)] [[PDF](https://arxiv.org/abs/2210.06101)] [[CODE](https://github.com/raipranav/fcl-fedseit)] | | Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling | SNU | EMNLP | 2022 | [[PUB](https://aclanthology.org/2022.emnlp-main.6/)] [[PDF](https://arxiv.org/abs/2204.14017)] | | A Federated Approach to Predicting Emojis in Hindi Tweets | University of Alberta | EMNLP | 2022 | [[PUB](https://aclanthology.org/2022.emnlp-main.819)] [[PDF](https://arxiv.org/abs/2211.06401)] [[CODE](https://github.com/deep1401/fedmoji)] | | Federated Model Decomposition with Private Vocabulary for Text Classification | HIT; Peng Cheng Lab | EMNLP | 2022 | [[PUB](https://aclanthology.org/2022.emnlp-main.430)] [[CODE](https://github.com/SMILELab-FL/FedVocab)] | -| Federated Meta-Learning for Emotion and Sentiment Aware Multi-modal Complaint Identification | | EMNLP | 2022 | [[PUB](https://openreview.net/forum?id=rVgVJ9eWxM9)] | -| Fair NLP Models with Differentially Private Text Encoders | | EMNLP | 2022 | [[PUB](https://openreview.net/forum?id=BVgNSki6q1c)] [[PDF](https://arxiv.org/abs/2205.06135)] [[CODE](https://github.com/saist1993/dpnlp)] | +| Fair NLP Models with Differentially Private Text Encoders | Univ. Lille | EMNLP | 2022 | [[PUB](https://aclanthology.org/2022.findings-emnlp.514/)] [[PDF](https://arxiv.org/abs/2205.06135)] [[CODE](https://github.com/saist1993/dpnlp)] | +| Federated Continual Learning for Text Classification via Selective Inter-client Transfer | DRIMCo GmbH; LMU | EMNLP Findings | 2022 | [[PUB](https://aclanthology.org/2022.findings-emnlp.353)] [[PDF](https://arxiv.org/abs/2210.06101)] [[CODE](https://github.com/raipranav/fcl-fedseit)] | +| Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation **`kg.`** | Lehigh University | EMNLP Findings | 2022 | [[PUB](https://aclanthology.org/2022.findings-emnlp.43/)] [[PDF](https://arxiv.org/abs/2203.09553)] [[CODE](https://github.com/taokz/FedR)] | +| Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation | PKU | EMNLP Findings | 2022 | [[PUB](https://aclanthology.org/2022.findings-emnlp.25/)] [[PDF](https://arxiv.org/abs/2210.06894)] | | Scaling Language Model Size in Cross-Device Federated Learning | Google | ACL workshop | 2022 | [[PUB](https://aclanthology.org/2022.fl4nlp-1.2/)] [[PDF](https://arxiv.org/abs/2204.09715)] | | Intrinsic Gradient Compression for Scalable and Efficient Federated Learning | Oxford | ACL workshop | 2022 | [[PUB](https://aclanthology.org/2022.fl4nlp-1.4/)] [[PDF](https://arxiv.org/abs/2112.02656)] | | ActPerFL: Active Personalized Federated Learning | Amazon | ACL workshop | 2022 | [[PUB](https://aclanthology.org/2022.fl4nlp-1.1)] [[PAGE](https://www.amazon.science/publications/actperfl-active-personalized-federated-learning)] | @@ -1035,7 +1064,6 @@ Federated Learning papers accepted by top AI and NLP conference and journal, inc | Federated Learning with Noisy User Feedback | USC; Amazon | NAACL | 2022 | [[PUB](https://aclanthology.org/2022.naacl-main.196/)] [[PDF](https://arxiv.org/abs/2205.03092)] | | Training Mixed-Domain Translation Models via Federated Learning | Amazon | NAACL | 2022 | [[PUB](https://aclanthology.org/2022.naacl-main.186)] [[PAGE](https://www.amazon.science/publications/training-mixed-domain-translation-models-via-federated-learning)] [[PDF](https://arxiv.org/abs/2205.01557)] | | Pretrained Models for Multilingual Federated Learning | Johns Hopkins University | NAACL | 2022 | [[PUB](https://aclanthology.org/2022.naacl-main.101)] [[PDF](https://arxiv.org/abs/2206.02291)] [[CODE](https://github.com/orionw/multilingual-federated-learning)] | -| Training Mixed-Domain Translation Models via Federated Learning | Amazon | NAACL | 2022 | [[PUB](https://aclanthology.org/2022.naacl-main.186/)] [[PAGE](https://www.amazon.science/publications/training-mixed-domain-translation-models-via-federated-learning)] [[PDF](https://arxiv.org/abs/2205.01557)] | | Federated Chinese Word Segmentation with Global Character Associations | University of Washington | ACL workshop | 2021 | [[PUB](https://aclanthology.org/2021.findings-acl.376)] [[CODE](https://github.com/cuhksz-nlp/GCASeg)] | | Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation | USTC | EMNLP | 2021 | [[PUB](https://aclanthology.org/2021.emnlp-main.223)] [[PDF](https://arxiv.org/abs/2109.05446)] [[CODE](https://github.com/yjw1029/Efficient-FedRec)] [[VIDEO](https://aclanthology.org/2021.emnlp-main.223.mp4)] | | Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories | CUHK (Shenzhen) | EMNLP | 2021 | [[PUB](https://aclanthology.org/2021.emnlp-main.321/)] [[CODE](https://github.com/cuhksz-nlp/ASA-TM)] [[VIDEO](https://aclanthology.org/2021.emnlp-main.321.mp4)] | @@ -1088,7 +1116,7 @@ Federated Learning papers accepted by top Database conference and journal, inclu - [SIGMOD](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fsigmod%3A) [2022](https://2022.sigmod.org/sigmod_research_list.shtml), [2021](https://2021.sigmod.org/sigmod_research_list.shtml) - [ICDE](https://dblp.uni-trier.de/search?q=federate%20venue%3AICDE%3A) [2023](https://icde2023.ics.uci.edu/papers-research-track/), [2022](https://icde2022.ieeecomputer.my/accepted-research-track/), [2021](https://ieeexplore.ieee.org/xpl/conhome/9458599/proceeding) -- [VLDB](https://dblp.org/search?q=federated%20streamid%3Ajournals%2Fpvldb%3A) 2023, [2022](https://vldb.org/pvldb/vol16-volume-info/), [2021](https://vldb.org/pvldb/vol15-volume-info/), [2021](http://www.vldb.org/pvldb/vol14/), [2020](http://vldb.org/pvldb/vol13-volume-info/) +- [VLDB](https://dblp.org/search?q=federated%20streamid%3Ajournals%2Fpvldb%3A) [2023](https://vldb.org/pvldb/volumes/17), [2022](https://vldb.org/pvldb/vol16-volume-info/), [2021](https://vldb.org/pvldb/vol15-volume-info/), [2021](http://www.vldb.org/pvldb/vol14/), [2020](http://vldb.org/pvldb/vol13-volume-info/)
fl in top db conference and journal @@ -1105,6 +1133,11 @@ Federated Learning papers accepted by top Database conference and journal, inclu | Distribution-Regularized Federated Learning on Non-IID Data | BUAA | ICDE | 2023 | [[PUB](https://ieeexplore.ieee.org/document/10184650)] | | Fed-SC: One-Shot Federated Subspace Clustering over High-Dimensional Data | ShanghaiTech University | ICDE | 2023 | [[PUB](https://ieeexplore.ieee.org/document/10184550)] [[CODE](https://github.com/SongjieXie/Fed-SC)] | | FLBooster: A Unified and Efficient Platform for Federated Learning Acceleration | ZJU | ICDE | 2023 | [[PUB](https://ieeexplore.ieee.org/document/10184883)] | +| FedGTA: Topology-aware Averaging for Federated Graph Learning. | BIT | VLDB | 2023 | [PUB](https://www.vldb.org/pvldb/vol17/p41-li.pdf) [CODE](https://github.com/xkLi-Allen/FedGTA) | +| FS-Real: A Real-World Cross-Device Federated Learning Platform. | Alibaba Group | VLDB | 2023 | [PUB](https://www.vldb.org/pvldb/vol16/p4046-chen.pdf) [PDF](https://arxiv.org/abs/2303.13363) [CODE](https://github.com/alibaba/FederatedScope/tree/FSreal) | +| Federated Calibration and Evaluation of Binary Classifiers. | meta | VLDB | 2023 | [PUB](https://www.vldb.org/pvldb/vol16/p3253-cormode.pdf) [PDF](https://arxiv.org/abs/2210.12526) [CODE](https://figshare.com/s/607998e479b0778645f6) | +| Olive: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification. | Kyoto University | VLDB | 2023 | [PUB](https://www.vldb.org/pvldb/vol16/p2404-kato.pdf) [PDF](https://arxiv.org/abs/2202.07165) [CODE](https://github.com/FumiyukiKato/FL-TEE) | +| Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System. | NUS | VLDB | 2023 | [PUB](https://www.vldb.org/pvldb/vol16/p2471-ooi.pdf) [CODE](https://github.com/nusdbsystem/falcon) | | Differentially Private Vertical Federated Clustering. | Purdue University | VLDB | 2023 | [[PUB](https://www.vldb.org/pvldb/vol16/p1277-li.pdf)] [[PDF](https://arxiv.org/abs/2208.01700)] [[CODE](https://anonymous.4open.science/r/public_vflclustering-63CD/README.md)] | | FederatedScope: A Flexible Federated Learning Platform for Heterogeneity. :fire: | Alibaba | VLDB | 2023 | [[PUB](https://www.vldb.org/pvldb/vol16/p1059-li.pdf)] [[PDF](https://arxiv.org/abs/2204.05011)] [[CODE](https://github.com/alibaba/FederatedScope)] | | Secure Shapley Value for Cross-Silo Federated Learning. | Kyoto University | VLDB | 2023 | [[PUB](https://www.vldb.org/pvldb/vol16/p1657-zheng.pdf)] [[PDF](https://arxiv.org/abs/2209.04856)] [[CODE](https://github.com/teijyogen/secsv)] | @@ -1293,7 +1326,9 @@ Federated Learning papers accepted by top Database conference and journal, inclu | FLINT: A Platform for Federated Learning Integration | LinkedIn | MLSys | 2023 | [[PUB](https://proceedings.mlsys.org/paper_files/paper/2023/hash/d3313de3f431fd64513431c4326d237c-Abstract-mlsys2023.html)] [[PDF](https://arxiv.org/abs/2302.12862)] | | On Noisy Evaluation in Federated Hyperparameter Tuning | CMU | MLSys | 2023 | [[PUB](https://proceedings.mlsys.org/paper_files/paper/2023/hash/294f82c43d69f66c04440cbb2740e52d-Abstract-mlsys2023.html)] [[PDF](https://arxiv.org/abs/2212.08930)] [[CODE](https://github.com/imkevinkuo/noisy-eval-in-fl)] | | GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning | UBC | MLSys | 2023 | [[PUB](https://proceedings.mlsys.org/paper_files/paper/2023/hash/3ed923f9f88108cb066c6568d3df2666-Abstract-mlsys2023.html)] [[PDF](https://arxiv.org/abs/2212.01523)] [[CODE](https://github.com/TCtower/GlueFL)] | +| Self-Supervised On-Device Federated Learning From Unlabeled Streams. | FDU | TCAD | 2023 | [[PUB](https://ieeexplore.ieee.org/document/10128673)] [PDF](https://arxiv.org/abs/2212.01006) | | Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing | ECNU | TCAD | 2023 | [[PUB](https://ieeexplore.ieee.org/document/9882092)] | +| Lightweight Blockchain-Empowered Secure and Efficient Federated Edge Learning | University of Exeter | TC | 2023 | [[PUB](https://ieeexplore.ieee.org/document/10177803)] | | Towards Data-Independent Knowledge Transfer in Model-Heterogeneous Federated Learning | PolyU | TC | 2023 | [[PUB](https://ieeexplore.ieee.org/document/10115052)] | | A New Federated Scheduling Algorithm for Arbitrary-Deadline DAG Tasks | NEFU | TC | 2023 | [[PUB](https://ieeexplore.ieee.org/document/10043684)] | | Privacy-Enhanced Decentralized Federated Learning at Dynamic Edge | SDU | TC | 2023 | [[PUB](https://ieeexplore.ieee.org/document/10025677)] | @@ -1302,6 +1337,7 @@ Federated Learning papers accepted by top Database conference and journal, inclu | Accelerating Federated Learning With a Global Biased Optimiser | University of Exeter | TC | 2023 | [[PUB](https://ieeexplore.ieee.org/document/9913718)] [[PDF](https://arxiv.org/abs/2108.09134)] [[CODE](https://github.com/jedmills/fedgbo)] | | Type-Aware Federated Scheduling for Typed DAG Tasks on Heterogeneous Multicore Platforms | TU Dortmund University | TC | 2023 | [[PUB](https://ieeexplore.ieee.org/document/9869701)] [[CODE](https://github.com/Strange369/TypedDAG_on_HeteroMP)] | | Sandbox Computing: A Data Privacy Trusted Sharing Paradigm Via Blockchain and Federated Learning. | BUPT | TC | 2023 | [[PUB](https://ieeexplore.ieee.org/document/9791849/)] | +| CHEESE: Distributed Clustering-Based Hybrid Federated Split Learning Over Edge Networks | SUDA | TPDS | 2023 | [[PUB](https://ieeexplore.ieee.org/document/10274134)] | | Hierarchical Federated Learning With Momentum Acceleration in Multi-Tier Networks | University of Sydney | TPDS | 2023 | [[PUB](https://ieeexplore.ieee.org/document/10180030)] [[PDF](https://arxiv.org/abs/2210.14560)] | | Dap-FL: Federated Learning Flourishes by Adaptive Tuning and Secure Aggregation | Xidian University | TPDS | 2023 | [[PUB](https://ieeexplore.ieee.org/document/10103633)] [[PDF](https://arxiv.org/abs/2206.03623)] [[CODE](https://github.com/XDUJiaweiChen/Dap-FL)] | | Collaborative Intrusion Detection System for SDVN: A Fairness Federated Deep Learning Approach | Anhui University | TPDS | 2023 | [[PUB](https://ieeexplore.ieee.org/document/10177377)] | @@ -1939,6 +1975,7 @@ This section partially refers to [The Federated Learning Portal](https://federat ![](https://img.shields.io/github/last-commit/youngfish42/Awesome-FL) +- *2023/12/13 - add MM, CCS, EMNLP 2023 papers and update VLDB, TC, TCAD papers* - *2023/10/07 - add ICCV 2023 papers* - *2023/09/18 - add INFOCOM, S&P 2023 papers* - *2023/08/18 - add COLT 2023 paper*