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Federated Reinforcement Learning: Techniques, Applications, and Open Challenges
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This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL), an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of Federated Learning (FL) and RL, we then focus on the introduction of FRL as a new method with great potential by leveraging the basic idea of FL to improve the performance of RL while preserving data-privacy. According to the distribution characteristics of the agents in the framework, FRL algorithms can be divided into two categories, i.e. Horizontal Federated Reinforcement Learning (HFRL) and Vertical Federated Reinforcement Learning (VFRL). We provide the detailed definitions of each category by formulas, investigate the evolution of FRL from a technical perspective, and highlight its advantages over previous RL algorithms. In addition, the existing works on FRL are summarized by application fields, including edge computing, communication, control optimization, and attack detection. Finally, we describe and discuss several key research directions that are crucial to solving the open problems within FRL.
Forward citations
Cited by 5 Pith papers
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FERMI-6G, a federated multi-agent DRQN framework with secure aggregation, reportedly improves latency, energy, reliability, and fairness over centralized and heuristic baselines in a simulated 6G edge network.
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A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes
A review of multi-agent reinforcement learning that catalogues federated, decentralized cooperative, and noncooperative regimes from the existing literature.
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