Federated Reinforcement Learning for Distributed Network Optimization
Dr. Maria Rodriguez, Dr. James Liu, Prof. Andrea Goldsmith
Stanford University
Abstract
We present a federated reinforcement learning framework that enables distributed network optimization without sharing raw network data between nodes. Our approach combines federated averaging with deep Q-networks, allowing each network node to learn locally while benefiting from global knowledge aggregation. Results on a large-scale simulated 5G network show 25% improvement in overall network throughput while maintaining strict data privacy guarantees.
AI Summary
- Proposes federated deep Q-networks for privacy-preserving distributed network optimization.
- Achieves 25% throughput improvement in simulated 5G networks while maintaining data privacy.
- Introduces a novel gradient compression technique reducing communication overhead by 90%.
- Demonstrates convergence guarantees under non-IID data distributions typical in telecom.
Key Findings
- 1Federated RL achieves 95% of centralized RL performance while keeping data local.
- 2Gradient compression enables practical deployment even with limited backhaul bandwidth.
- 3The framework scales linearly with the number of network nodes.
Industry Implications
Enables AI-driven optimization in multi-operator environments where data sharing is restricted.
Applicable to 6G network slicing optimization across distributed edge nodes.
Addresses regulatory requirements for data sovereignty in telecom AI deployments.
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