FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs
arXiv:2605.21264v1 Announce Type: new Abstract: Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving distributed learning. However, existing FL methods face a fundamental challenge. Traditional averaging-based approaches suffer from parameter divergence under non-IID conditions, while personalized FL methods overfit to local data and fail to generalize to new clients (cold-start problem). Mixture-of-Experts naturally addresses this by routing heterogeneous data to specialized experts rather than forcing uniform aggregation. In this paper, we propose FedCoE, a Fede
The proliferation of distributed data sources and growing privacy concerns are pushing advancements in federated learning paradigms.
Improving federated learning's ability to balance generalization and personalization is critical for developing more robust and adaptable AI systems that can operate across diverse, decentralized data environments.
This research proposes a new approach (FedCoE) to overcome key limitations in existing federated learning methods, improving model performance in non-IID conditions and addressing the cold-start problem.
- · AI researchers
- · Organizations with distributed data
- · Privacy-focused AI applications
- · Traditional centralized AI models
More efficient and privacy-preserving AI models can be deployed across various industries without centralizing sensitive data.
Enhanced federated learning could accelerate the development of personalized AI services that maintain high generalization capabilities.
The widespread adoption of improved federated learning techniques might reduce the need for large, centralized data lakes, shifting compute and data strategies.
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