arXiv:2506.22427v2 Announce Type: replace-cross Abstract: We propose CLoVE (Clustering of Loss Vector Embeddings), a novel algorithm for Clustered Federated Learning (CFL). In CFL, clients are naturally grouped into clusters based on their data distribution. However, identifying these clusters is challenging, as client assignments are unknown. CLoVE utilizes client embeddings derived from model losses on client data, and leverages the insight that clients in the same cluster share similar loss values, while those in different clusters exhibit distinct loss patterns. Based on these embeddings,
The proliferation of federated learning in complex, distributed AI systems necessitates sophisticated methods for handling data heterogeneity and privacy concerns.
This development improves personalized AI model training in federated settings, enhancing efficiency and accuracy for various applications without centralizing sensitive data.
The ability to automatically cluster clients based on loss patterns allows for more targeted and effective federated model personalization, reducing the need for explicit client grouping.
- · AI developers
- · Healthcare providers
- · Edge computing platforms
- · Data privacy advocates
- · Traditional centralized AI training models
Improved performance and personalization of federated learning applications are achieved through intelligent client clustering.
Broader adoption of federated learning across industries that deal with sensitive, decentralized data.
Accelerated development of AI agents capable of learning and adapting highly personalized models across diverse user bases while maintaining privacy.
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Read at arXiv cs.AI