What is it about?

This study proposes a novel method to make federated reinforcement learning (FRL) more communication-efficient. In FRL, multiple agents learn together without sharing raw data by sending updates to a central server. However, the frequent exchange of large neural network models leads to high communication costs. To solve this, the authors design a layer-wise selective compression method that prioritizes important layers and selectively compresses less critical ones using quantization and sparsification techniques. The method ensures model performance is maintained while drastically reducing the size of communicated data.

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Why is it important?

As FRL systems are deployed in real-world distributed environments like smart grids, intelligent transportation, and mobile edge computing, communication bandwidth becomes a major bottleneck. Reducing the communication burden without compromising learning performance is critical for scalability and efficiency. This paper provides a systematic solution that balances model quality and communication cost, pushing FRL closer to large-scale deployment in bandwidth-limited environments.

Perspectives

I find this research particularly meaningful because it addresses one of the core challenges in making federated learning viable for reinforcement learning tasks—communication bottlenecks. By introducing a layer-wise importance-guided strategy, the authors offer an elegant solution that remains agnostic to model architecture and task type. It paves the way for more energy-efficient and scalable FRL systems, especially relevant to edge AI applications where both computing and network resources are constrained.

Professor/Clarivate Highly Cited Researcher/Associate Editor of IEEE TSG/TII/TSTE Yang Li
Northeast Electric Power University

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This page is a summary of: Boosting Communication Efficiency in Federated Learning for Multiagent-Based Multimicrogrid Energy Management, IEEE Transactions on Neural Networks and Learning Systems, May 2025, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/tnnls.2024.3432137.
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