OracleFed: Latency-Aware Federated Learning via Dynamic Recovery and Causal Aggregation

As federated learning (FL) continues to expand across wide-area, heterogeneous networks, preserving true training chronology grows complex, particularly in time-sensitive applications where network delays can obscure causal ordering and degrade global-model fidelity. Current aggregation schemes seldom account for temporal dynamics: they either stall on stragglers, discard late updates, or rely on arrival-based heuristics that conflate communication latency with training staleness. In light of this limitation, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OracleFed—Order-Respecting Aggregation with Causality and Latency Equalization</i>, a framework in which training-time lag is decoupled from network-transport delay and elevated as the primary driver of aggregation. Leveraging a hybrid design that integrates dual timestamping with system-wide synchronization, the framework continuously recalibrates the server’s waiting window and re-indexes arrivals by their generation timestamps, thereby preserving causal order. Rather than treating late arrivals as inherently stale, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OracleFed</i> discounts only genuine training staleness, allowing high-latency clients to contribute proportionally to their computational freshness. We benchmark the proposed framework against existing synchronous and asynchronous schemes across eight evaluation metrics in a distributed, in-vehicle emotion-recognition setting. Paired <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i>- andWilcoxon signed-rank analyses of twenty Monte-Carlo replicates verify that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OracleFed</i> combines the coordination advantages of both paradigms and achieves higher predictive quality, lower staleness, and fairer client participation than the baselines.