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

arXiv: https://arxiv.org/abs/2506.09660 · HTML: https://mlsanity.com/paper/2506.09660/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [Asynchronous Federated Learning on Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2109.04269/) (2021)
- [FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-aware Model Update](https://mlsanity.com/paper/2312.05770/) (2023)
- [Arena: A Learning-based Synchronization Scheme for Hierarchical Federated Learning--Technical Report](https://mlsanity.com/paper/2308.10298/) (2023)
- [FedFa: A Fully Asynchronous Training Paradigm for Federated Learning](https://mlsanity.com/paper/2404.11015/) (2024)
- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://mlsanity.com/paper/2109.03999/) (2021)
- [Delay Sensitive Hierarchical Federated Learning with Stochastic Local Updates](https://mlsanity.com/paper/2302.04851/) (2023)
- [Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives](https://mlsanity.com/paper/2304.08602/) (2023)
- [FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting](https://mlsanity.com/paper/2406.02877/) (2024)
- [Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning](https://mlsanity.com/paper/2210.10311/) (2022)
- [Exploring Age-of-Information Weighting in Federated Learning under Data Heterogeneity](https://mlsanity.com/paper/2405.15978/) (2024)

## Built on by (most cited first, 4 of 4)

- [FedMultiEmo: Real-Time Emotion Recognition via Multimodal Federated Learning](https://mlsanity.com/paper/2507.15470/) (2025)
- [AlignFed: Alignment-Aware Asynchronous Federated Fine-Tuning for Large Language Models in Heterogeneous Edge Environments](https://mlsanity.com/paper/2606.08197/) (2026)
- [FedRAW: Preserving Rare-Label Influence in Asynchronous Federated Learning](https://mlsanity.com/paper/2609.07192/) (2026)
- [QoS-Aware Federated Learning for Multimodal In-Cabin Interaction in Smart Vehicles](https://mlsanity.com/paper/2609.20123/) (2026)

## Abstract (arXiv)

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 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 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.