Secure Decentralized Federated Learning via Gossip and Virtual Voting
Decentralized federated learning (DFL) removes the central server by letting nodes exchange model updates through peer-to-peer gossip, but existing gossip-based methods often lack provenance finality and resilience to Byzantine or lazy participants. Ledger-assisted federated learning (FL) improves auditability, yet blockchains, shards, or settlement committees can reintroduce global coordination costs that conflict with DFL locality. This paper proposes \emph{gspDAG-FL}, a secure DFL framework that derives consensus from the same gossip history used to disseminate models. Nodes exchange model
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- LinkedLinked via arxiv author · 85%Amirhossein Taherpour →
“Secure Decentralized Federated Learning via Gossip and Virtual Voting”
- LinkedLinked via arxiv author · 85%Xiaodong Wang →
“Secure Decentralized Federated Learning via Gossip and Virtual Voting”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Secure Decentralized Federated Learning via Gossip and Virtu” ≈ “aymericdamien/TopDeepLearning””
- PossiblePossibly related (embedding) · 47%Optimized IoT clustering and assignment in semi-synchronous federated learning - Nature →