Cascade: Enhancing Reinforcement Learning with Curriculum Federated Learning and Interference Avoidance — A Case Study in Adaptive Bitrate Selection

Current reinforcement learning (RL) algorithms, particularly RL-based networking algorithms, demonstrate significant potential for overcoming limitations of manually-tuned heuristics. However, RL-based algorithms are known to be sample inefficient and may not perform well in a wide range of environments. Federated reinforcement learning (FRL) aims to enhance sample efficiency and improve model performance across a wide variety of environments. Nevertheless, many existing approaches neglect the challenges posed by the dynamic nature of training sample distributions in RL and the heterogeneity of data across clients in FRL. This may restrain the broader applicability of FRL algorithms. Addressing these gaps, we propose Cascade, the first curriculum federated reinforcement learning framework with interference avoidance, and we study Cascade in the context of RL-based adaptive bitrate (ABR) selection algorithms. To eliminate interference between two or more interfering tasks from different clients in FRL, we propose an interference avoidance technique that penalizes changes to model parameters important for other clients. We extensively evaluate Cascade in a wide range of network environments. Our experiments show that Cascade outperforms the state-of-the-art federated learning settings by a minimum of 21% in average reward and 15% in model skewness. These findings highlight the efficacy of Cascade and underscore the potential of enhancing FRL.