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2021

  1. Sageflow: Robust Federated Learning against Both Stragglers and Adversaries
  2. Catastrophic Data Leakage in Vertical Federated Learning
  3. Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
  4. Optimality and Stability in Federated Learning: A Game-theoretic Approach
  5. QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning
  6. The Skellam Mechanism for Differentially Private Federated Learning
  7. No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
  8. STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning
  9. Subgraph Federated Learning with Missing Neighbor Generation
  10. Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
  11. Personalized Federated Learning With Gaussian Processes
  12. Differentially Private Federated Bayesian Optimization with Distributed Exploration
  13. Parameterized Knowledge Transfer for Personalized Federated Learning
  14. Federated Reconstruction: Partially Local Federated Learning
  15. Fast Federated Learning in the Presence of Arbitrary Device Unavailability
  16. FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective
  17. FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
  18. Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients
  19. Federated Multi-Task Learning under a Mixture of Distributions
  20. Federated Graph Classification over Non-IID Graphs
  21. Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
  22. On Large-Cohort Training for Federated Learning
  23. DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning
  24. PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization
  25. Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis
  26. Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
  27. Federated Linear Contextual Bandits
  28. Few-Round Learning for Federated Learning
  29. Breaking the centralized barrier for cross-device federated learning
  30. Federated-EM with heterogeneity mitigation and variance reduction
  31. Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning
  32. FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization

2020

  1. Federated Accelerated Stochastic Gradient Descent
  2. Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
  3. Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
  4. Robust Federated Learning The Case of Affine Distribution Shifts
  5. FedSplit: an algorithmic framework for fast federated optimization
  6. Throughput-Optimal Topology Design for Cross-Silo Federated Learning
  7. Ensemble Distillation for Robust Model Fusion in Federated Learning
  8. Lower Bounds and Optimal Algorithms for Personalized Federated Learning
  9. Federated Principal Component Analysis
  10. An Efficient Framework for Clustered Federated Learning
  11. Inverting Gradients - How easy is it to break privacy in federated learning?
  12. Differentially-Private Federated Linear Bandits
  13. Personalized Federated Learning with Moreau Envelopes
  14. Distributionally Robust Federated Averaging
  15. Federated Bayesian Optimization via Thompson Sampling
  16. Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach
  17. Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge