Robust Decentralized Learning Using ADMM With Unreliable Agents

Many signal processing and machine learning problems can be formulated as consensus optimization problems which can be solved efficiently via a cooperative multi-agent system. However, the agents in the system can be unreliable due to a variety of reasons: noise, faults and attacks. Providing errone...

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Veröffentlicht in:	IEEE transactions on signal processing 2022, Vol.70 (N/A), p.2743-2757
Hauptverfasser:	Li, Qunwei, Kailkhura, Bhavya, Goldhahn, Ryan, Ray, Priyadip, Varshney, Pramod K.
Format:	Artikel
Sprache:	eng
Schlagworte:	ADMM Algorithms Convergence Convex functions Decentralized learning Machine learning MATHEMATICS AND COMPUTING Multiagent systems Neighborhoods Network design Neural networks Optimization Peer-to-peer computing Performance degradation Robustness Signal processing Signal processing algorithms Training unreliable
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