Integrated Sensing-Communication-Computation-Based Online Federated Learning With Limited Cache in OFDM Systems

This paper proposes a new cache-assisted integrated sensing-communication-computation (ISCC) framework for efficient online federated edge learning (FEEL) in orthogonal frequency division multiple (OFDM) systems. The framework innovatively leverages both real-time sensory data and selectively cached historical data to enhance learning efficiency, particularly in scenarios with constrained availability of new data and limited capacity of device cache in broadband channels. However, this approach introduces critical challenges that the concept drift in historical data degrades model relevance, and the joint optimization of sensing, communication, and computation resources becomes more complex due to the interplay between new and old data utilization. To address these challenges, we first establish a comprehensive system architecture detailing online sensing, cached data utilization, local computation, and global model aggregation. Then, a rigorous theoretical convergence analysis using the first-order Taylor approximation is conducted. Via alternating optimization, a cache-assisted ISCC scheme is proposed to maximize per-round loss reduction under stringent latency, energy, and cache constraints. Extensive simulations on a human motion recognition task validate the theoretical convergence analysis and the superiority of the proposed loss-aware caching design over baselines.