auto update @ 2024-05-20T09:58:15Z Asia/Shanghai

youngfish42 · May 20, 2024 · fc7d387 · fc7d387
1 parent e8c1b9e
commit fc7d387
Show file tree

Hide file tree

Showing 2 changed files with 275 additions and 33 deletions.
diff --git a/README.md b/README.md
@@ -465,10 +465,10 @@ Federated Learning papers accepted by top ML(machine learning) conference and jo
 
 |Title                                                           |    Affiliation                                                     |    Venue             |    Year    |    Materials|
 | ------------------------------------------------------------ | ------------------------------------------------------------ | -------------- | ---- | ------------------------------------------------------------ |
-|Communication-efficient clustered federated learning via model distance | USTC; State Key Laboratory of Cognitive Intelligence | Mach Learn | 2024 | [PUB](https://link.springer.com/article/10.1007/s10994-023-06443-5) |
+| Communication-efficient clustered federated learning via model distance | USTC; State Key Laboratory of Cognitive Intelligence | Mach Learn | 2024 | [[PUB](https://link.springer.com/article/10.1007/s10994-023-06443-5)] |
 | Federated learning with superquantile aggregation for heterogeneous data. | Google Research | Mach Learn | 2024 | [[PUB](https://link.springer.com/article/10.1007/s10994-023-06332-x)] [[PDF](https://arxiv.org/abs/2112.09429)] [[CODE](https://github.com/krishnap25/simplicial-fl)] |
 | Aligning model outputs for class imbalanced non-IID federated learning | NJU | Mach Learn | 2024 | [[PUB](https://link.springer.com/article/10.1007/s10994-022-06241-5)] |
-| Federated Gaussian Process: Convergence, Automatic Personalization and Multi-Fidelity Modeling | Northeastern University; UoM | TPAMI | 2024 | [PUB](https://ieeexplore.ieee.org/document/10402074) [PDF](https://arxiv.org/abs/2111.14008) [CODE](https://github.com/UMDataScienceLab/Federated_Gaussian_Process) |
+| Federated Gaussian Process: Convergence, Automatic Personalization and Multi-Fidelity Modeling | Northeastern University; UoM | TPAMI | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10402074)] [[PDF](https://arxiv.org/abs/2111.14008)] [[CODE](https://github.com/UMDataScienceLab/Federated_Gaussian_Process)] |
 | The Impact of Adversarial Attacks on Federated Learning: A Survey | IIT | TPAMI | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10274102)] |
 | Understanding and Mitigating Dimensional Collapse in Federated Learning | NUS | TPAMI | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10336535)] [[PDF](https://arxiv.org/abs/2210.00226)] [[CODE](https://github.com/bytedance/FedDecorr)] |
 | No One Left Behind: Real-World Federated Class-Incremental Learning | CAS; UCAS | TPAMI | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10323204)] [[PDF](https://arxiv.org/abs/2302.00903)] [[CODE](https://github.com/JiahuaDong/LGA)] |
@@ -1491,35 +1491,35 @@ Federated Learning papers accepted by top Database conference and journal, inclu
 
 |Title                                                           |    Affiliation                                                     |    Venue         |    Year    |    Materials|
 | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- | ---- | ------------------------------------------------------------ |
-|Accelerating the Decentralized Federated Learning via Manipulating Edges | SZU | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645509) |
-|Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation | SDNU | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645337) [PDF](https://arxiv.org/abs/2401.14678) [CODE](https://github.com/ckano/pfcr) |
-|PAGE: Equilibrate Personalization and Generalization in Federated Learning | XDU | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645513) [PDF](https://arxiv.org/abs/2310.08961) [CODE](https://github.com/ivy-h7/PAGE) |
-|Federated Learning Vulnerabilities: Privacy Attacks with Denoising Diffusion Probabilistic Models | ECNU | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645514) |
-|Co-clustering for Federated Recommender System | UIUC | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645626) |
-|Incentive and Dynamic Client Selection for Federated Unlearning | BUPT | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645462) |
-|Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training | VinUniversity | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645702) [PDF](https://arxiv.org/abs/2401.03748) [CODE](https://github.com/nnhieu/colr-fedrec) |
-|BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework | ZJU | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645425) [PDF](https://arxiv.org/abs/2205.10568) |
-|Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation | UQ | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645690) [PDF](https://arxiv.org/abs/2401.17630) |
-|FedDSE: Distribution-aware Sub-model Extraction for Federated Learning over Resource-constrained Devices | NTU | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645416) |
-|Cardinality Counting in "Alcatraz": A Privacy-aware Federated Learning Approach | CSIRO’s Data61 | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645655) |
-|Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation | BUPT | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645693) [PDF](https://arxiv.org/abs/2310.11730) |
-|Poisoning Federated Recommender Systems with Fake Users | USTC | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645492) [PDF](https://arxiv.org/abs/2402.11637) |
-|Towards Energy-efficient Federated Learning via INT8-based Training on Mobile DSPs | BUPT | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645341) |
-|Privacy-Preserving and Fairness-Aware Federated Learning for Critical Infrastructure Protection and Resilience | UTS | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645545) [CODE](https://github.com/CGD-release/cgd) |
-|When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions | JLU | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645525) [PDF](https://arxiv.org/abs/2305.12650) [CODE](https://github.com/Zhangcx19/IFedRec) |
-|How Few Davids Improve One Goliath: Federated Learning in Resource-Skewed Edge Computing Environments | UCSD | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645544) [CODE](https://github.com/jiayunz/RecipFL) [VIDEO](https://www.youtube.com/watch?v=nN1UjYw_6uQ) |
-|Poisoning Attack on Federated Knowledge Graph Embedding | PolyU | WWW | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589334.3645422) [CODE](https://github.com/jisooma/FKGEPoison) |
-|FL@FM-TheWebConf'24: International Workshop on Federated Foundation Models for the Web | CUHK | WWW (Companion Volume) | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589335.3641298) [PAGE](https://federated-learning.org/fl@fm-www-2024/) |
-|An Investigation into the Feasibility of Performing Federated Learning on Social Linked Data Servers | University of Southampton | WWW (Companion Volume) | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589335.3651950) |
-|Exploring Representational Similarity Analysis to Protect Federated Learning from Data Poisoning | SYSU | WWW (Companion Volume) | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589335.3651503) |
-|Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation | Purdue University | WWW (Companion Volume) | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589335.3651931) [PDF](https://arxiv.org/abs/2401.07456) |
-|FedHLT: Efficient Federated Low-Rank Adaption with Hierarchical Language Tree for Multilingual Modeling | CUHK | WWW (Companion Volume) | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589335.3651933) |
-|HBIAS FedAvg: Smooth Federated Learning Transition for In-use Edge Models | IIT | WWW (Companion Volume) | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589335.3651518) |
-|Phoenix: A Federated Generative Diffusion Model | UW | WWW (Companion Volume) | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589335.3651935) |
-|Federated Learning in Large Model Era: Vision-Language Model for Smart City Safety Operation Management | ENN; UPC | WWW (Companion Volume) | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589335.3651939) |
-|Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks | USTC | WWW (Companion Volume) | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589335.3651555) [PDF](https://arxiv.org/abs/2403.03149) |
-|GradFilt: Class-wise Targeted Data Reconstruction from Gradients in Federated Learning | PolyU | WWW (Companion Volume) | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589335.3651514) |
-|Detecting Poisoning Attacks on Federated Learning Using Gradient-Weighted Class Activation Mapping | ISEP | WWW (Companion Volume) | 2024 | [PUB](https://dl.acm.org/doi/10.1145/3589335.3651490) |
+| Accelerating the Decentralized Federated Learning via Manipulating Edges | SZU | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645509)] |
+| Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation | SDNU | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645337)] [[PDF](https://arxiv.org/abs/2401.14678)] [[CODE](https://github.com/ckano/pfcr)] |
+| PAGE: Equilibrate Personalization and Generalization in Federated Learning | XDU | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645513)] [[PDF](https://arxiv.org/abs/2310.08961)] [[CODE](https://github.com/ivy-h7/PAGE)] |
+| Federated Learning Vulnerabilities: Privacy Attacks with Denoising Diffusion Probabilistic Models | ECNU | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645514)] |
+| Co-clustering for Federated Recommender System | UIUC | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645626)] |
+| Incentive and Dynamic Client Selection for Federated Unlearning | BUPT | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645462)] |
+| Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training | VinUniversity | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645702)] [[PDF](https://arxiv.org/abs/2401.03748)] [[CODE](https://github.com/nnhieu/colr-fedrec)] |
+| BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework | ZJU | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645425)] [[PDF](https://arxiv.org/abs/2205.10568)] |
+| Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation | UQ | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645690)] [[PDF](https://arxiv.org/abs/2401.17630)] |
+| FedDSE: Distribution-aware Sub-model Extraction for Federated Learning over Resource-constrained Devices | NTU | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645416)] |
+| Cardinality Counting in "Alcatraz": A Privacy-aware Federated Learning Approach | CSIRO’s Data61 | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645655)] |
+| Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation | BUPT | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645693)] [[PDF](https://arxiv.org/abs/2310.11730)] |
+| Poisoning Federated Recommender Systems with Fake Users | USTC | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645492)] [[PDF](https://arxiv.org/abs/2402.11637)] |
+| Towards Energy-efficient Federated Learning via INT8-based Training on Mobile DSPs | BUPT | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645341)] |
+| Privacy-Preserving and Fairness-Aware Federated Learning for Critical Infrastructure Protection and Resilience | UTS | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645545)] [[CODE](https://github.com/CGD-release/cgd)] |
+| When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions | JLU | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645525)] [[PDF](https://arxiv.org/abs/2305.12650)] [[CODE](https://github.com/Zhangcx19/IFedRec)] |
+| How Few Davids Improve One Goliath: Federated Learning in Resource-Skewed Edge Computing Environments | UCSD | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645544)] [[CODE](https://github.com/jiayunz/RecipFL)] [[VIDEO](https://www.youtube.com/watch?v=nN1UjYw_6uQ)] |
+| Poisoning Attack on Federated Knowledge Graph Embedding | PolyU | WWW | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589334.3645422)] [[CODE](https://github.com/jisooma/FKGEPoison)] |
+| FL@FM-TheWebConf'24: International Workshop on Federated Foundation Models for the Web | CUHK | WWW (Companion Volume) | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589335.3641298)] [[PAGE](https://federated-learning.org/fl@fm-www-2024/)] |
+| An Investigation into the Feasibility of Performing Federated Learning on Social Linked Data Servers | University of Southampton | WWW (Companion Volume) | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589335.3651950)] |
+| Exploring Representational Similarity Analysis to Protect Federated Learning from Data Poisoning | SYSU | WWW (Companion Volume) | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589335.3651503)] |
+| Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation | Purdue University | WWW (Companion Volume) | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589335.3651931)] [[PDF](https://arxiv.org/abs/2401.07456)] |
+| FedHLT: Efficient Federated Low-Rank Adaption with Hierarchical Language Tree for Multilingual Modeling | CUHK | WWW (Companion Volume) | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589335.3651933)] |
+| HBIAS FedAvg: Smooth Federated Learning Transition for In-use Edge Models | IIT | WWW (Companion Volume) | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589335.3651518)] |
+| Phoenix: A Federated Generative Diffusion Model | UW | WWW (Companion Volume) | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589335.3651935)] |
+| Federated Learning in Large Model Era: Vision-Language Model for Smart City Safety Operation Management | ENN; UPC | WWW (Companion Volume) | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589335.3651939)] |
+| Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks | USTC | WWW (Companion Volume) | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589335.3651555)] [[PDF](https://arxiv.org/abs/2403.03149)] |
+| GradFilt: Class-wise Targeted Data Reconstruction from Gradients in Federated Learning | PolyU | WWW (Companion Volume) | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589335.3651514)] |
+| Detecting Poisoning Attacks on Federated Learning Using Gradient-Weighted Class Activation Mapping | ISEP | WWW (Companion Volume) | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3589335.3651490)] |
 | AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving | NTU | MobiCom | 2023 | [[PUB](https://dl.acm.org/doi/10.1145/3570361.3592517)] [[PDF](https://arxiv.org/abs/2302.08646)] |
 | Efficient Federated Learning for Modern NLP | Beiyou                                        Shenzhen Institute | MobiCom | 2023 | [[PDF](https://arxiv.org/abs/2205.10162)] [[解读](https://zhuanlan.zhihu.com/p/638270746)] |
 | FLASH: Towards a High-performance Hardware Acceleration Architecture for Cross-silo Federated Learning | HKUST; Clustar | NSDI | 2023 | [[PUB](https://www.usenix.org/conference/nsdi23/presentation/zhang-junxue)] [[SLIDE](https://www.usenix.org/system/files/nsdi23_slides_zhang.pdf)] [[VIDEO](https://www.youtube.com/watch?v=I5V3r-8sY-Y)] |
@@ -1652,8 +1652,8 @@ Federated Learning papers accepted by top Database conference and journal, inclu
 | FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN |  | EuroSys workshop | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3642968.3654813)] |
 | ALS Algorithm for Robust and Communication-Efficient Federated Learning |  | EuroSys workshop | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3642970.3655842)] |
 | FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission. |  | EuroSys workshop | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3642970.3655834)] |
-| Taking Advantage of the Mistakes: Rethinking Clustered Federated Learning for IoT Anomaly Detection | UVIC | TPDS | 2024 | [PUB](https://ieeexplore.ieee.org/document/10476751) |
-| FedICT: Federated Multi-Task Distillation for Multi-Access Edge Computing | UCAS | TPDS | 2024 | [PUB](https://ieeexplore.ieee.org/document/10163770) [PDF](http://export.arxiv.org/abs/2301.00389) |
+| Taking Advantage of the Mistakes: Rethinking Clustered Federated Learning for IoT Anomaly Detection | UVIC | TPDS | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10476751)] |
+| FedICT: Federated Multi-Task Distillation for Multi-Access Edge Computing | UCAS | TPDS | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10163770)] [[PDF](http://export.arxiv.org/abs/2301.00389)] |
 | Collaboration in Federated Learning With Differential Privacy: A Stackelberg Game Analysis | SYSU | TPDS | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10400897)] |
 | FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training | USTC | TPDS | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10323207)] |
 | EcoFed: Efficient Communication for DNN Partitioning-Based Federated Learning | University of St Andrews | TPDS | 2024 | [[PUB](https://ieeexplore.ieee.org/document/10380682)] [[PDF](https://arxiv.org/abs/2304.05495)] [[CODE](https://github.com/blessonvar/ecofed)] |