Enhancing Consistent Federated Learning Objectives Through Uniform Feature Distributions
Siqi Deng, Yang Liu · 2024
Federated Learning is a distributed paradigm that facilitates collaborative training of deep models among multiple parties without exchanging raw data. However, the common non-independent and identically distributed (Non-IID) data distribution among clients introduces discrepancies between local training objectives and the global goal. This misalignment results in a slow convergence of global model and a decrease in generalization performance. We propose a method to enhance consistent federated learning objectives through uniform and consistent feature distributions (FedUF). FedUF effectively captures the feature variation space rich in semantic information and integrates implicit semantic data augmentation and logits adjustment to establish a uniform and consistent global feature distribution. Through lightweight yet innovative adjustments to the client-side objective functions, we formulate a globally consistent objective function. The mutual promotion between globally consistent feature distribution and the objective function significantly alleviates the impact of Non-IID data, greatly enhancing the overall performance of Federated Learning. Moreover, extensive experiments conducted on Cifar10 and Cifar100 datasets convincingly validate the effectiveness of the proposed FedUF.