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Low-Latency Federated Reinforcement Learning-Based Resource Allocation in Converged Access Networks

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Abstract

We propose a federated reinforcement learning (FedRL) solution to innovate resource allocation in converged access networks. FedRL lowers network latency with reinforcement-learnt bandwidth decision and achieves fast learning with federated learning efforts.

© 2020 The Author(s)

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