arXiv:2504.17471v2 Announce Type: replace Abstract: Gossip Learning (GL) is a decentralized learning paradigm where users iteratively exchange and aggregate models with a small set of neighboring peers. Recent approaches rely on dynamic communication graphs built using Random Peer Sampling (RPS) protocols which have been proven to accelerate convergence. However, we show that these approaches are vulnerable to a dual attack: Byzantine nodes can poison models and manipulate peer sampling to amplify their influence. We address this combination of threats with GRANITE, a framework for robust lear
The increasing deployment of decentralized AI systems, particularly in sensitive or adversarial environments, necessitates robust security measures against sophisticated attacks that combine data poisoning with network manipulation.
This research addresses fundamental vulnerabilities in decentralized AI, preventing malicious actors from corrupting learning processes and amplifying their influence, which is crucial for the integrity and trustworthiness of future AI systems.
The GRANITE framework introduces a new mitigation strategy, allowing for more secure and reliable decentralized learning in the presence of Byzantine attacks, enabling wider adoption of such systems in critical applications.
- · Developers of decentralized AI systems
- · Sectors requiring secure distributed computation (e.g., defense, finance)
- · Users of AI systems relying on federated or gossip learning
- · Malicious actors attempting to poison distributed AI models
- · Current vulnerable gossip learning protocols
Wider adoption of Byzantine-resilient protocols in decentralized AI frameworks.
Increased trust and deployment of collaborative AI in sensitive applications and competitive environments.
Potential for new AI-powered security services that monitor and defend against advanced adversarial attacks on distributed learning.
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Read at arXiv cs.LG