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Found 3 papers, 0 papers with code
PROFL: A Privacy-Preserving Federated Learning Method with Stringent Defense Against Poisoning Attacks
Federated Learning (FL) faces two major issues: privacy leakage and poisoning attacks, which may seriously undermine the reliability and security of the system.
Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response
With this knowledge we propose a class of perturbed SBRD with the following property: only policies with maximum metric are observed with nonzero probability for a broad class of stochastic games with finite memory.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Person Re-Identification by Unsupervised Video Matching
Crucially, this model does not require pairwise labelled training data (i. e. unsupervised) therefore readily scalable to large scale camera networks of arbitrary camera pairs without the need for exhaustive data annotation for every camera pair.
