AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning

By Young Geun Kim, Carole-Jean Wu. 2021

117 citations recorded by the source corpus.

Federated learning enables a cluster of decentralized mobile devices at the edge to collaboratively train a shared machine learning model, while keeping all the raw training samples on device. This decentralized training approach is demonstrated as a practical solution to mitigate the risk of privacy leakage. However, enabling efficient FL deployment at the edge is challenging because of non-IID training data distribution, wide system heterogeneity and stochastic-varying runtime effects in the field. This paper jointly optimizes time-to-convergence and energy efficiency of state-of-the-art FL use cases by taking into account the stochastic nature of edge execution. We propose AutoFL by tailor-designing a reinforcement learning algorithm that learns and determines which K participant devices and per-device execution targets for each FL model aggregation round in the presence of stochastic runtime variance, system and data heterogeneity. By considering the unique characteristics of FL edge deployment judiciously, AutoFL achieves 3.6 times faster model convergence time and 4.7 and 5.2 times higher energy efficiency for local clients and globally over the cluster of K participants, respectively.

Subfields: Artificial Intelligence, Computer Networks and Communications, Efficiency & Compression

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Citations in the corpus

Sustainable AI: Environmental Implications, Challenges and Opportunities (2021, background): “Optimizing the overall energy efficiency of FL and on-device AI is an important first step [92], [93], [94], [95], [96].”
Measuring the Carbon Intensity of AI in Cloud Instances (2022, methodology): “We see various ways in which our tool can help guide model training, for instance via carbon-informed optimization (similarly to what [22] proposed for energy efficiency in federated learning), or for cloud-based recommendations that enable users to opt-in for carbon-aware configurations (in terms o”
Agentic Software Issue Resolution with Large Language Models: A Survey (2025, unknown): “…[32] collects four categories of issue data, including bug reports, feature requests, security issues, and performance issues, and applies traditional fault localization evaluation methods [84, 127, 167] to assess the fine-grained performance of LLMs in localizing different types of issues.”