EnCAgg filters malicious gradients in federated learning by projecting updates to two divergent dimensions for density clustering, generating boundary pseudo-gradients to link outliers, and re-clustering to recover benign updates even with unknown variable attackers.
InProceedings of the 2023 ACM Asia Conference on Computer and Communications Security(Melbourne, VIC, Australia)(ASIA CCS ’23)
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AgentReputation proposes separating AI agent task execution, reputation management, and secure record-keeping into distinct layers, with context-specific reputation cards and a risk-based policy engine to handle verification in decentralized settings.
citing papers explorer
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EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
EnCAgg filters malicious gradients in federated learning by projecting updates to two divergent dimensions for density clustering, generating boundary pseudo-gradients to link outliers, and re-clustering to recover benign updates even with unknown variable attackers.
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AgentReputation: A Decentralized Agentic AI Reputation Framework
AgentReputation proposes separating AI agent task execution, reputation management, and secure record-keeping into distinct layers, with context-specific reputation cards and a risk-based policy engine to handle verification in decentralized settings.