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Deep reinforcement learning (DRL) has demonstrated significant potential in various applications, including gaming AI, robotics, and system scheduling. DRL algorithms produce, sample, and learn from training data online through a trial-and-error process, demanding considerable time and computational resources. To address this, distributed DRL algorithms and paradigms have been developed to expedite training using extensive resources. Through carefully designed experiments, we are the first to observe that strategically increasing the actor-environment interactions by spawning more concurrent actors at certain training rounds within ephemeral time frames can significantly enhance training efficiency. Yet, current distributed DRL solutions, which are predominantly server-based (or serverful), fail to capitalize on these opportunities due to their long startup times, limited adaptability, and cumbersome scalability. This paper proposesNitro, a generic training engine for distributed DRL algorithms that enforces timely and effective boosting with concurrent actors instantaneously spawned by serverless computing. With serverless functions,Nitroadjusts data sampling strategies dynamically according to the DRL training demands.Nitroseizes the opportunity of real-time boosting by accurately and swiftly detecting an empirical metric. To achieve cost efficiency, we design a heuristic actor scaling algorithm to guideNitrofor cost-aware boosting budget allocation. We integrateNitrowith state-of-the-art DRL algorithms and frameworks and evaluate them on AWS EC2 and Lambda. Experiments with Mujoco and Atari benchmarks show thatNitroimproves the final rewards (i.e., training quality) by up to 6× and reduces training costs by up to 42%. 

As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices' computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, it is unaware of (i) the dynamic changes of devices' communication and computing conditions and (ii) FL training progress and its dynamic requirements of local training contributions, both of which may cause very long FL training delay. Motivated by those dynamics, in this paper, we develop a wireless and heterogeneity aware latency efficient FL (WHALE-FL) approach to accelerate FL training through adaptive subnetwork scheduling. Instead of sticking to the fixed size subnetwork, WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides the mobile device to adaptively select the subnetwork size for local training based on (a) its computing and communication capacity, (b) its dynamic computing and/or communication conditions, and (c) FL training status and its corresponding requirements for local training contributions. Our evaluation shows that, compared with peer designs, WHALE-FL effectively accelerates FL training without sacrificing learning accuracy.