SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronization

TL;DR

SyncFed introduces a time-aware federated learning framework that uses explicit timestamping and synchronization to improve model convergence and accuracy in distributed environments with network delays and clock differences.

Contribution

It proposes a novel synchronization mechanism using timestamps and NTP to quantify staleness and enhance aggregation in federated learning.

Findings

Improved model accuracy over baseline methods.

Enhanced information freshness in the global model.

Stable temporal evolution of the model in distributed settings.

Abstract

As Federated Learning (FL) expands to larger and more distributed environments, consistency in training is challenged by network-induced delays, clock unsynchronicity, and variability in client updates. This combination of factors may contribute to misaligned contributions that undermine model reliability and convergence. Existing methods like staleness-aware aggregation and model versioning address lagging updates heuristically, yet lack mechanisms to quantify staleness, especially in latency-sensitive and cross-regional deployments. In light of these considerations, we introduce \emph{SyncFed}, a time-aware FL framework that employs explicit synchronization and timestamping to establish a common temporal reference across the system. Staleness is quantified numerically based on exchanged timestamps under the Network Time Protocol (NTP), enabling the server to reason about the relative freshness of client updates and apply temporally informed weighting during aggregation. Our empirical evaluation on a geographically distributed testbed shows that, under \emph{SyncFed}, the global model evolves within a stable temporal context, resulting in improved accuracy and information freshness compared to round-based baselines devoid of temporal semantics.