Secure Decentralized Federated Learning via Gossip and Virtual Voting

This paper presents a federated learning framework that enables secure, decentralized model training across distributed participants using gossip protocols and virtual voting mechanisms, eliminating the need for a central server. The approach addresses key challenges in privacy-preserving collaborative machine learning, particularly relevant for regulated industries that require data to remain on-premises while benefiting from collective model improvement.

This paper presents a federated learning framework that enables secure, decentralized model training across distributed participants using gossip protocols and virtual voting mechanisms, eliminating the need for a central server. The approach addresses key challenges in privacy-preserving collaborative machine learning, particularly relevant for regulated industries that require data to remain on-premises while benefiting from collective model improvement.

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