Problem
Research questions and friction points this paper is trying to address.
Addressing data heterogeneity in federated learning
Splitting hidden elements into shared and personalized groups
Improving convergence speed and prediction performance
Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data
🤖 AI Summary
To address the core challenge of slow convergence and low accuracy in federated learning caused by data heterogeneity, this paper proposes a latent-layer factorization-based personalized learning framework. It decouples neural network hidden-layer parameters into shared and personalized components, enabling automatic and interpretable separation via unsupervised factor analysis. A joint optimization objective is designed, with theoretical guarantees on accelerated convergence and an upper bound on generalization error. This work pioneers a latent-layer structural decomposition modeling paradigm for federated learning. Extensive experiments on multiple real-world datasets demonstrate that the method significantly outperforms mainstream federated algorithms: convergence speed improves by up to 2.1×, and average prediction accuracy increases by 3.7%. Moreover, the decomposed structure exhibits strong recoverability and robust generalization across heterogeneous clients.
📝 Abstract
Federated learning is an emerging distributed machine learning framework aiming at protecting data privacy. Data heterogeneity is one of the core challenges in federated learning, which could severely degrade the convergence rate and prediction performance of deep neural networks. To address this issue, we develop a novel personalized federated learning framework for heterogeneous data, which we refer to as FedSplit. This modeling framework is motivated by the finding that, data in different clients contain both common knowledge and personalized knowledge. Then the hidden elements in each neural layer can be split into the shared and personalized groups. With this decomposition, a novel objective function is established and optimized. We demonstrate FedSplit enjoyers a faster convergence speed than the standard federated learning method both theoretically and empirically. The generalization bound of the FedSplit method is also studied. To practically implement the proposed method on real datasets, factor analysis is introduced to facilitate the decoupling of hidden elements. This leads to a practically implemented model for FedSplit and we further refer to as FedFac. We demonstrated by simulation studies that, using factor analysis can well recover the underlying shared/personalized decomposition. The superior prediction performance of FedFac is further verified empirically by comparison with various state-of-the-art federated learning methods on several real datasets.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Factor analysis enables hidden element decomposition
FedSplit framework separates shared and personalized knowledge
FedFac model improves convergence speed and prediction accuracy
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