Accelerating Fair Federated Learning: Adaptive Federated Adam

TL;DR

This paper introduces Adaptive Federated Adam (AdaFedAdam), an optimizer designed to improve fairness and convergence in federated learning, especially under non-IID data distributions, by addressing bias and optimizing multi-objective fairness.

Contribution

The paper proposes AdaFedAdam, a novel optimizer that accelerates fair federated learning and reduces bias, validated through experiments demonstrating superior performance over existing methods.

Findings

AdaFedAdam achieves better convergence than baseline algorithms.

It provides improved fairness across participants in federated learning.

The method is robust and Pareto optimal in numerical evaluations.

Abstract

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.