Fairness and Accuracy in Federated Learning

Abstract

In the federated learning setting, multiple clients jointly train a model under the coordination of the central server, while the training data is kept on the client to ensure privacy. Normally, inconsistent distribution of data across different devices in a federated network and limited communication bandwidth between end devices impose both statistical heterogeneity and expensive communication as major challenges for federated learning. This paper proposes an algorithm to achieve more fairness and accuracy in federated learning (FedFa). It introduces an optimization scheme that employs a double momentum gradient, thereby accelerating the convergence rate of the model. An appropriate weight selection algorithm that combines the information quantity of training accuracy and training frequency to measure the weights is proposed. This procedure assists in addressing the issue of unfairness in federated learning due to preferences for certain clients. Our results show that the proposed FedFa algorithm outperforms the baseline algorithm in terms of accuracy and fairness.

References in corpus (16)

• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Federated Learning: Strategies for Improving Communication Efficiency
• Federated Learning with Non-IID Data
• A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus
• Revisiting Distributed Synchronous SGD
• Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation
• Improving Federated Learning Personalization via Model Agnostic Meta Learning
• Fair Resource Allocation in Federated Learning
• Agnostic Federated Learning
• Personalized Federated Learning with Moreau Envelopes
• Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
• Overcoming Forgetting in Federated Learning on Non-IID Data
• Variational Federated Multi-Task Learning
• Federated Learning with Matched Averaging
• Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs

Cited by in corpus (5)

• The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning
• Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets
• Federated Learning with Fair Averaging
• Demystifying the Effects of Non-Independence in Federated Learning
• Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare