AdaCLP: Efficient Federated Learning for Asynchronous Mobile Devices with Temporally Imbalanced Data

Federated learning (FL) enables mobile devices to collaboratively train deep learning models while maintaining data privacy and minimizing communication overhead. However, traditional FL methods typically assume access to pre-collected datasets, an impractical assumption for real-world mobile devices that continuously collect sensor data streams without retaining them while operating, due to limited memory constraints. Streaming Federated Learning (SFL) partially addresses this limitation by supporting online learning and asynchronous model aggregation directly from live data streams. Yet, a critical challenge in mobile data streams is the temporal class imbalance, which may biase the streaming FL process toward early-arriving data classes during the critical learning period (CLP), the initial training phase when the model exhibits the highest plasticity. To address this, we propose AdaCLP, a training scheduler that tracks global model plasticity using a staleness-decayed mechanism specifically designed for the dynamics of real-world mobile environments. AdaCLP dynamically adjusts local training hyperparameters in SFL to prolong the CLP, thereby preserving model plasticity in the face of time-varying, imbalanced data distributions. Furthermore, a CLP-aware dynamic voltage and frequency scaling (DVFS) strategy is integrated to reduce the energy cost of prolonged training, aligning with the tight energy budgets of mobile devices. Experimental results demonstrate that AdaCLP can effectively handle either temporally imbalanced or periodic data, improving accuracy by up to 11% while reducing energy consumption by 69.5% compared to state-of-the-art methods, without modifying the standard FL training pipeline. These demonstrate that AdaCLP is a practical and efficient solution for real-world mobile FL deployment, enabling robust on-device adaptation to evolving data streams.

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