mlsanityData to 7 Oct 2026

AdaFed: Fair Federated Learning via Adaptive Common Descent Direction

arXiv 2401.04993 ↗First posted 10 Jan 2024cs.LG cs.AIcited by 12

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Abstract (arXiv)

Federated learning (FL) is a promising technology via which some edge devices/clients collaboratively train a machine learning model orchestrated by a server. Learning an unfair model is known as a critical problem in federated learning, where the trained model may unfairly advantage or disadvantage some of the devices. To tackle this problem, in this work, we propose AdaFed. The goal of AdaFed is to find an updating direction for the server along which (i) all the clients' loss functions are decreasing; and (ii) more importantly, the loss functions for the clients with larger values decrease with a higher rate. AdaFed adaptively tunes this common direction based on the values of local gradients and loss functions. We validate the effectiveness of AdaFed on a suite of federated datasets, and demonstrate that AdaFed outperforms state-of-the-art fair FL methods.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 34 of its 53 references that are in the map. First posted on arXiv 10 Jan 2024.

Builds on

34 of 53 references.

References · 34

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22 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 4; 2026: 1.

12 papers.

  1. 2024Bayes Conditional Distribution Estimation for Knowledge Distillation Based on Conditional Mutual Informationcited by 25
  2. 2025Distributed Quasi-Newton Method for Fair and Fast Federated Learningcited by 4
  3. 2024Rate-Constrained Quantization for Communication-Efficient Federated Learningcited by 3
  4. 2025Over-the-Air Fair Federated Learning via Multi-Objective Optimizationcited by 3
  5. 2024Adversarial Training via Adaptive Knowledge Amalgamation of an Ensemble of Teacherscited by 2
  6. 2024GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learningcited by 1
  7. 2025Achieving Distributive Justice in Federated Learning via Uncertainty Quantificationcited by 1
  8. 2024Revisiting Min-Max Optimization Problem in Adversarial Trainingcited by 0
  9. 2024RFID based Health Adherence Medicine Case Using Fair Federated Learningcited by 0
  10. 2024Federated Learning Approach to Mitigate Water Wastagecited by 0
  11. 2025FAIRVAR: Fair Federated Learning via Variance Regularizationcited by 0
  12. 2026FairMean: Promoting Fairness in Distributed Learning under Label Poisoning Attackscited by 0