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

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 relia...

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Bibliographic Details
Published in:Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) pp. 1 - 8
Main Authors: Gul, Baran Can, Tziampazis, Stefanos, Jazdi, Nasser, Weyrich, Michael
Format: Conference Proceeding
Language:English
Published: IEEE 09.09.2025
Subjects:
Delays
Distributed Training
Federated learning
Manufacturing automation
Network Time Protocol
Numerical models
Protocols
Reliability
Semantics
Servers
Synchronization
Temporal Consistency
Timestamp-based Aggregation
Training
ISSN:1946-0759
Online Access:Get full text
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Summary: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 SyncFed, a time-aware FL framework that employs explicit synchronization and times-tamping 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 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.
ISSN:1946-0759
DOI:10.1109/ETFA65518.2025.11205776