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HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning
Such fragmentation introduces a new type of data heterogeneity in FL, namely \textit{system-induced data heterogeneity}, as each device generates distinct data depending on its hardware and software configurations.
FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning
Federated learning (FL) has emerged as a solution to deal with the risk of privacy leaks in machine learning training.
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning
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.
AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance
Such execution scaling decision becomes more complicated with the stochastic nature of mobile-cloud execution, where signal strength variations of the wireless networks and resource interference can significantly affect real-time inference performance and system energy efficiency.
