Final papers of FL-ICML’21

  1. A New Analysis Framework for Federated Learning on Time-Evolving Heterogeneous Data
    Yongxin Guo, Tao Lin and Xiaoying Tang
  2. A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning
    Xinyi Xu and Lingjuan Lyu
  3. Accelerating Federated Learning with Split Learning on Locally Generated Losses
    Dong-Jun Han, Hasnain Irshad Bhatti, Jungmoon Lee and Jaekyun Moon
  4. Achieving Optimal Sample and Communication Complexities for Non-IID Federated Learning
    Prashant Khanduri, Pranay Sharma, Haibo Yang, Mingyi Hong, Jia Liu, Ketan Rajawat and Pramod K Varshney
  5. Bi-directional Adaptive Communication for Heterogenous Distributed Learning
    Dmitrii Avdiukhin, Nikita Ivkin, Sebastian U Stich and Vladimir Braverman
  6. BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning
    Pengwei Xing, Songtao Lu, Lingfei Wu and Han Yu
  7. BYGARS: Byzantine SGD with Arbitrary Number of Attackers Using Reputation Scores
    Jayanth Regatti, Hao Chen and Abhishek Gupta
  8. Byzantine Fault-Tolerance of Local Gradient-Descent in Federated Model under 2f-Redundancy
    Nirupam Gupta, Thinh Doan and Nitin Vaidya
  9. Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding (full text is temporarily hidden per authors’ request)
    Hankyul Baek, Won Joon Yun, Jihong Park, Soyi Jung, Joongheon Kim, Mingyue Ji and Mehdi Bennis
  10. Decentralized federated learning of deep neural networks on non-iid data
    Edvin Listo Zec, Noa Onoszko, Gustav Karlsson and Olof Mogren
  11. Defending against Reconstruction Attack in Vertical Federated Learning
    Jiankai Sun, Yuanshun Yao, Weihao Gao, Junyuan Xie and Chong Wang
  12. Diverse Client Selection for Federated Learning: Submodularity and Convergence Analysis
    Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith and Jeff Bilmes
  13. EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback
    Peter Richtarik, Igor Sokolov and Ilyas Fatkhullin
  14. Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
    Xiaolin Chen, Shuai Zhou, Kai Yang, Hao Fan, Zejin Feng, Zhong Chen, Yongji Wang and Hu Wang
  15. Federated Graph Classification over Non-IID Graphs
    Han Xie, Jing Ma, Li Xiong and Carl Yang
  16. Federated Learning with Buffered Asynchronous Aggregation
    John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Michael Rabbat, Mani Malek and Dzmitry Huba
  17. Federated Learning with Metric Loss
    Hyunsin Park, Hossein Hosseini and Sungrack Yun
  18. Federated Multi-Task Learning under a Mixture of Distributions
    Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni and Richard Vidal
  19. Federated Random Reshuffling with Compression and Variance Reduction
    Grigory Malinovsky and Peter Richtárik
  20. FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation
    Chuhan Wu, Fangzhao Wu, Yang Cao, Lingjuan Lyu, Yongfeng Huang and Xing Xie
  21. FedMix: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning
    Elnur Gasanov, Ahmed Khaled, Samuel Horvath and Peter Richtarik
  22. FedNL: Making Newton-Type Methods Applicable to Federated Learning
    Mher Safaryan, Rustem Islamov, Xun Qian and Peter Richtarik
  23. FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
    Samuel Horvath, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris and Nicholas Lane
  24. FlyNN: Fruit-fly Inspired Federated Nearest Neighbor Classification
    Parikshit Ram and Kaushik Sinha
  25. Gradient Inversion with Generative Image Prior
    Jinwoo Jeon, Jaechang Kim, Kangwook Lee, Sewoong Oh and Jungseul Ok
  26. GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization
    Yen-Hsiu Chou, Shenda Hong, Chenxi Sun, Derun Cai, Moxian Song and Hongyan Li
  27. Handling Both Stragglers and Adversaries for Robust Federated Learning
    Jungwuk Park, Dong-Jun Han, Minseok Choi and Jaekyun Moon
  28. Implicit Gradient Alignment in Distributed and Federated Learning
    Yatin Dandi, Luis Barba and Martin Jaggi
  29. Local Adaptivity in Federated Learning: Convergence and Consistency
    Jianyu Wang, Zheng Xu, Zachary Garrett, Zachary Charles, Luyang Liu and Gauri Joshi
  30. Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization over Time-Varying Networks
    Dmitry Kovalev, Elnur Gasanov, Peter Richtarik and Alexander Gasnikov
  31. Multistage stepsize schedule in Federated Learning: Bridging Theory and Practice
    Charlie Hou, Kiran Thekumparampil, Giulia Fanti and Sewoong Oh
  32. MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization
    Laurent Condat and Peter Richtárik
  33. New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning
    Siddharth Divi, Yi-Shan Lin, Habiba Farrukh and Z Berkay Celik
  34. OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning
    Jiacheng Liang, Wensi Jiang and Songze Li
  35. On Large-Cohort Training for Federated Learning
    Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian and Virginia Smith
  36. Optimal Model Averaging: Towards Personalized Collaborative Learning
    Felix Grimberg, Mary-Anne Hartley, Sai Praneeth Karimireddy and Martin Jaggi
  37. Robust and Differentially Private Mean Estimation
    Xiyang Liu, Weihao Kong, Sham Kakade and Sewoong Oh
  38. Smoothness-Aware Quantization Techniques
    Bokun Wang, Mher Safaryan and Peter Richtarik
  39. SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
    Chaoyang He, Emir Ceyani, Keshav Balasubramanian, Murali Annavaram and Salman Avestimehr
  40. Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity
    Amirhossein Reisizadeh, Isidoros Tziotis, Hamed Hassani, Aryan Mokhtari and Ramtin Pedarsani
  41. Subgraph Federated Learning with Missing Neighbor Generation
    Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun and Siu Ming Yiu
  42. Towards Federated Learning With Byzantine-Robust Client Weighting
    Amit Portnoy, Yoav Tirosh and Danny Hendler
  43. Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy
    Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu and Jinfeng Yi