Distributed Quasi-Newton Method for Fair and Fast Federated Learning
Federated Learning;Fairness
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@misc{
hamidi2024distributed,
title={Distributed Quasi-Newton Method for Fair and Fast Federated Learning},
author={Shayan Mohajer Hamidi and Linfeng Ye},
year={2024},
url={https://openreview.net/forum?id=n6PE0xbgdA}
}
Federated Learning (FL) is a promising technology that enables edge devices/clients to collaboratively and iteratively train a machine learning model under the coordination of a central server. The most common approach to FL is first-order methods, where clients send their local gradients to the server in each iteration. However, these methods often suffer from slow convergence rates. As a remedy, second-order methods, such as quasi-Newton, can be employed to accelerate convergence. Unfortunately, similarly to the first-order FL methods, the application of second-order methods in FL can lead to unfair models, achieving high average accuracy while performing poorly on certain clients' local datasets. To tackle this issue, in this paper we introduce  a novel second-order FL framework, dubbed distributed quasi-Newton federated learning (DQN-Fed). This approach seeks to ensure fairness while leveraging the fast convergence properties of quasi-Newton methods in the FL context. Specifically, DQN-Fed helps the server update the global model in such a way that (i) all local loss functions decrease to promote fairness, and (ii) the rate of change in local loss functions aligns with that of the quasi-Newton method. We prove the convergence of DQN-Fed and demonstrate its linear-quadratic convergence rate. Moreover, we validate the efficacy of DQN-Fed across a range of federated datasets, showing that it surpasses state-of-the-art fair FL methods in both fairness and convergence speed. The Code for paper is publicly available at https://github.com/ICMLDQNFed/ICMLDQN.
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~Shayan_Mohajer_Hamidi1 ~Linfeng_Ye1
Shayan Mohajer Hamidi Linfeng Ye
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Federated Learning Fairness
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hamidi|distributed_quasinewton_method_for_fair_and_fast_federated_learning
/pdf/317aa6ac42cc85d79ee4f71499b374c9c840b4d1.pdf
unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Distributed Quasi-Newton Method for Fair and Fast Federated Learning
ICLR 2025 Conference Desk Rejected Submission
ICLR.cc/2025/Conference/Desk_Rejected_Submission