Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering
arXiv:2303.04345v2 Announce Type: replace Abstract: Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients. However, FL suffers performance degradation from heterogeneous and limited data. To alleviate the degradation, we present a novel personalized Bayesian FL approach named pFedBayes. By using the trained global distribution from the server as the prior distribution of each client, each client adjusts its own distribution by minimizing the sum of the reconstruction error over its personalized data and the KL divergen
The increasing prevalence of distributed data and privacy concerns is driving rapid innovation in federated learning techniques.
This research offers a method to significantly enhance the performance and personalization of federated learning, addressing key limitations of data heterogeneity and sparsity.
Federated learning systems can now be more robust and adaptable to diverse client data, potentially broadening their application in sensitive domains.
- · AI developers
- · Privacy-focused industries
- · Edge computing providers
- · Centralized data aggregation models
Improved federated learning models will enable more effective AI deployments in privacy-sensitive sectors like healthcare and finance.
The ability to personalize models locally within a federated framework could lead to more tailored and efficient services for individual users.
Enhanced trust in federated AI systems might accelerate their adoption, potentially shifting data processing and AI model development towards a more distributed paradigm.
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Read at arXiv cs.LG