A cost-aware distributed online learning method with strict adversarial rejection and two-time-scale adaptive regulation attenuates evolution desynchronization in IoT multi-agent systems under persistent attacks.
Fast distributed optimization over directed graphs under malic ious attacks using trust
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Cost-Aware Distributed Online Learning with Strict Rejection Behavior against Adversarial Agents
A cost-aware distributed online learning method with strict adversarial rejection and two-time-scale adaptive regulation attenuates evolution desynchronization in IoT multi-agent systems under persistent attacks.