Federated Learning (FL) facilitates collaborative training of a global model whose performance is boosted by private data owned by distributed clients, without compromising data privacy. Yet the wide applicability of FL is hindered by entanglement of data distributions across different clients. This paper demonstrates for the first time that by disentangling data distributions FL can in principle achieve efficiencies comparable to those of distributed systems, requiring only one round of communication. To this end, we propose a novel FedDistr algorithm, which employs stable diffusion models to decouple and recover data distributions. Empirical results on the CIFAR100 and DomainNet datasets show that FedDistr significantly enhances model utility and efficiency in both disentangled and near-disentangled scenarios while ensuring privacy, outperforming traditional federated learning methods.

该研究解决了联邦学习中不同客户端之间数据分布纠缠的问题，限制了联邦学习的广泛应用。作者提出了一种新颖的FedDistr算法，通过稳定的扩散模型解耦和恢复数据分布，使得联邦学习在通信仅需一轮的情况下，能够实现与分布式系统相当的效率。实验证明，FedDistr显著提高了模型的效用和效率，同时确保隐私，优于传统的联邦学习方法。

联邦学习中的数据分布解耦