IEEE

Differentiable Logic Policy for Interpretable Deep Reinforcement Learning: A Study From an Optimization Perspective

Abstract: The interpretability of policies remains an important challenge in Deep Reinforcement Learning (DRL). This paper explores interpretable DRL via representing policy by Differentiable ...
IEEE

FedRDF: A Robust and Dynamic Aggregation Function Against Poisoning Attacks in Federated Learning

Abstract: Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages, FL is ...