arXiv:2607.08651v1 Announce Type: new Abstract: 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 u
The increasing scale and complexity of federated learning deployments, especially in sensitive domains, necessitate robust security and decentralization to overcome existing limitations.
This development addresses critical challenges in decentralized AI, offering enhanced security and resilience against adversarial or unreliable participants, which is crucial for real-world adoption.
The proposed 'gspDAG-FL' framework introduces a more secure and auditable approach to decentralized federated learning, potentially enabling broader and more trustworthy applications in AI.
- · privacy-sensitive AI applications
- · decentralized AI platforms
- · AI agents developers
- · edge computing infrastructure
- · centralized federated learning services
- · unsecured peer-to-peer AI systems
- · Byzantine actors in FL networks
Improved security and reliability of decentralized federated learning models accelerate adoption in enterprise and governmental sectors.
The reduced need for central coordination in federated learning lowers operational costs and enhances data sovereignty for participants.
The underlying cryptographic and consensus primitives could inspire similar secure decentralized approaches across other distributed computing paradigms.
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Read at arXiv cs.LG