mlsanityData to 7 Oct 2026

Accelerating Fair Federated Learning: Adaptive Federated Adam

arXiv 2301.09357 ↗First posted 23 Jan 2023cs.LG cs.DCcited by 8

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Abstract (arXiv)

Federated learning is a distributed and privacy-preserving approach to train a statistical model collaboratively from decentralized data of different parties. However, when datasets of participants are not independent and identically distributed (non-IID), models trained by naive federated algorithms may be biased towards certain participants, and model performance across participants is non-uniform. This is known as the fairness problem in federated learning. In this paper, we formulate fairness-controlled federated learning as a dynamical multi-objective optimization problem to ensure fair performance across all participants. To solve the problem efficiently, we study the convergence and bias of Adam as the server optimizer in federated learning, and propose Adaptive Federated Adam (AdaFedAdam) to accelerate fair federated learning with alleviated bias. We validated the effectiveness, Pareto optimality and robustness of AdaFedAdam in numerical experiments and show that AdaFedAdam outperforms existing algorithms, providing better convergence and fairness properties of the federated scheme.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 28 of its 44 references that are in the map. First posted on arXiv 23 Jan 2023.

Builds on

28 of 44 references.

References · 28

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  8. 2018Federated Learning with Non-IID Datacited by 1,099
  9. 2020Adaptive Federated Optimizationcited by 1,036
  10. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  11. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  12. 2019On the Convergence of Adam and Beyondcited by 834
16 more
  1. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 816
  2. 2018LEAF: A Benchmark for Federated Settingscited by 780
  3. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  4. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  5. 2020Federated Learning with Matched Averagingcited by 564
  6. 2019Agnostic Federated Learningcited by 451
  7. 2019Fair Resource Allocation in Federated Learningcited by 389
  8. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  9. 2019Tighter Theory for Local SGD on Identical and Heterogeneous Datacited by 266
  10. 2019On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimizationcited by 178
  11. 2020Mime: Mimicking Centralized Stochastic Algorithms in Federated Learningcited by 152
  12. 2019SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentumcited by 121
  13. 2020Federated Accelerated Stochastic Gradient Descentcited by 85
  14. 2020Federated Learning Meets Multi-objective Optimizationcited by 50
  15. 2021Scalable federated machine learning with FEDncited by 16
  16. 2019Path Length Bounds for Gradient Descent and Flowcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 3; 2026: 1.

8 papers.

  1. 2023Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learningcited by 12
  2. 2025DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Modelscited by 4
  3. 2024Review of Mathematical Optimization in Federated Learningcited by 3
  4. 2025Multi-objective methods in Federated Learning: A survey and taxonomycited by 3
  5. 2024Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous Clientscited by 1
  6. 2024Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizescited by 1
  7. 2025Achieving Distributive Justice in Federated Learning via Uncertainty Quantificationcited by 1
  8. 2026Distributed Direct Preference Optimizationcited by 0