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

• URL: http://arxiv.org/abs/2506.09660v1
• Date: Wed, 11 Jun 2025 12:29:20 GMT
• Title: SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronization
• Abstract summary: emphSyncFed is a time-aware FL framework that employs explicit synchronization and timestamping to establish a common temporal reference across the system.<n>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.<n>Our empirical evaluation on a geographically distributed testbed shows that, under emphSyncFed, the global model evolves within a stable temporal context, resulting in improved accuracy and information freshness.
• Score: 0.6749750044497732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching [15.279995924017458]
  TiWeaver is a unified framework designed to handle temporal dynamics and fine-grained inter-channel dependencies adaptively.<n>We evaluate TiWeaver on 12 real-world time series datasets, where it achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-06-02T04:06:12Z)
• TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecasting [6.1337977581640075]
  TimeSAF is a new framework based on hierarchical asynchronous fusion.<n>It provides stable and efficient semantic guidance while avoiding interference with low-level temporal dynamics.
  arXiv  Detail & Related papers  (2026-04-14T12:18:00Z)
• Discrete Prototypical Memories for Federated Time Series Foundation Models [62.22804699052539]
  Leveraging Large Language Models as federated learning (FL)-based time series foundation models offers a promising way to transfer the generalization capabilities of LLMs to time series data.<n>However, the semantic misalignment between time-series data and the text-centric latent space of existing LLMs often leads to degraded performance.<n>We propose textscFeDPM, a federated framework for time-series foundation models based on discrete prototypical memories.
  arXiv  Detail & Related papers  (2026-04-06T06:57:08Z)
• Revisiting Gradient Staleness: Evaluating Distance Metrics for Asynchronous Federated Learning Aggregation [1.5268922363885409]
  In asynchronous federated learning (FL), client devices send updates to a central server at varying times based on their computational speed.<n>This staleness can degrade the convergence and accuracy of the global model.<n>Previous work, such as AsyncFedED, proposed an adaptive aggregation method using Euclidean distance to measure staleness.<n>We extend this approach by exploring alternative distance metrics to more accurately capture the effect of gradient staleness.
  arXiv  Detail & Related papers  (2026-03-09T10:40:25Z)
• MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models [51.506429027626005]
  Memory for Time Series (MEMTS) is a lightweight and plug-and-play method for retrieval-free domain adaptation in time series forecasting.<n>Key component of MEMTS is a Knowledge Persistence Module (KPM), which internalizes domain-specific temporal dynamics.<n>This paradigm shift enables MEMTS to achieve accurate domain adaptation with constant-time inference and near-zero latency.
  arXiv  Detail & Related papers  (2026-02-14T14:00:06Z)
• Contextual and Seasonal LSTMs for Time Series Anomaly Detection [49.50689313712684]
  We propose a novel prediction-based framework named Contextual and Seasonal LSTMs (CS-LSTMs)<n>CS-LSTMs are built upon a noise decomposition strategy and jointly leverage contextual dependencies and seasonal patterns.<n>They consistently outperform state-of-the-art methods, highlighting their effectiveness and practical value in robust time series anomaly detection.
  arXiv  Detail & Related papers  (2026-02-10T11:46:15Z)
• Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting [7.6757168009144126]
  We propose TFMixer, a joint time-frequency modeling framework for IMTS forecasting.<n>Specifically, TFMixer incorporates a Global Frequency Module that employs a learnable Non-Uniform Discrete Fourier Transform (NUDFT) to directly extract spectral representations from irregular timestamps.<n>In parallel, the Local Time Module introduces a query-based patch mixing mechanism to adaptively aggregate informative temporal patches and alleviate information density imbalance.
  arXiv  Detail & Related papers  (2026-01-31T07:49:44Z)
• UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecasting [90.47915032778366]
  We propose UniDiff, a unified diffusion framework for multimodal time series forecasting.<n>At its core lies a unified and parallel fusion module, where a single cross-attention mechanism integrates structural information from timestamps and semantic context from texts.<n>Experiments on real-world benchmark datasets across eight domains demonstrate that the proposed UniDiff model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-12-08T05:36:14Z)
• TIMED: Adversarial and Autoregressive Refinement of Diffusion-Based Time Series Generation [0.31498833540989407]
  TIMED is a unified generative framework that captures global structure via a forward-reverse diffusion process.<n>To further align the real and synthetic distributions in feature space, TIMED incorporates a Maximum Mean Discrepancy (MMD) loss.<n>We show that TIMED generates more realistic and temporally coherent sequences than state-of-the-art generative models.
  arXiv  Detail & Related papers  (2025-09-23T23:05:40Z)
• TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding [3.64798801374117]
  TimeMosaic is a forecasting framework that aims to address temporal heterogeneity.<n>TimeMosaic employs adaptive patch embedding to dynamically adjust granularity according to local information density.<n>Our model trained on the large-scale corpus with 321 billion observations achieves performance competitive with state-of-the-art TSFMs.
  arXiv  Detail & Related papers  (2025-09-23T09:20:00Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [55.09326865401653]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• Adaptive Deadline and Batch Layered Synchronized Federated Learning [66.93447103966439]
  Federated learning (FL) enables collaborative model training across distributed edge devices while preserving data privacy, and typically operates in a round-based synchronous manner.<n>We propose ADEL-FL, a novel framework that jointly optimize per-round deadlines and user-specific batch sizes for layer-wise aggregation.
  arXiv  Detail & Related papers  (2025-05-29T19:59:18Z)
• FRAIN to Train: A Fast-and-Reliable Solution for Decentralized Federated Learning [1.1510009152620668]
  asynchronous learning (FL) enables collaborative model training across distributed clients while preserving data locality.<n>We introduce Fast-and-Reliable AI Network, FRAIN, a new FL method that mitigates these limitations by incorporating two key ideas.<n>Experiments with a CNN image-classification model and a Transformer-based language model demonstrate that FRAIN achieves more stable and robust convergence than FedAvg, FedAsync, and BRAIN.
  arXiv  Detail & Related papers  (2025-05-07T08:20:23Z)
• STRGCN: Capturing Asynchronous Spatio-Temporal Dependencies for Irregular Multivariate Time Series Forecasting [14.156419219696252]
  STRGCN captures the complex interdependencies in IMTS by representing them as a fully connected graph.<n>Experiments on four public datasets demonstrate that STRGCN achieves state-of-the-art accuracy, competitive memory usage and training speed.
  arXiv  Detail & Related papers  (2025-05-07T06:41:33Z)
• DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting [19.064628208136273]
  We propose a Relation and Adaptive Network Distribution (DRAN) capable of adapting to and distribution changes over time.<n>We show that our SFL efficiently preserves spatial relationships across various temporal operations.<n>Our approach outperforms state of the art methods in weather prediction and traffic forecasting flows.
  arXiv  Detail & Related papers  (2025-04-02T09:18:43Z)
• Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates [71.81037644563217]
  Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. We propose straggler-aware layer-wise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion.
  arXiv  Detail & Related papers  (2024-03-27T09:14:36Z)
• TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling [67.02157180089573]
  Time series pre-training has recently garnered wide attention for its potential to reduce labeling expenses and benefit various downstream tasks. This paper proposes TimeSiam as a simple but effective self-supervised pre-training framework for Time series based on Siamese networks.
  arXiv  Detail & Related papers  (2024-02-04T13:10:51Z)
• FAVANO: Federated AVeraging with Asynchronous NOdes [14.412305295989444]
  We propose a novel centralized Asynchronous Federated Learning (FL) framework, FAVANO, for training Deep Neural Networks (DNNs) in resource constrained environments.
  arXiv  Detail & Related papers  (2023-05-25T14:30:17Z)
• Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks [56.91063444859008]
  Federated Learning (FL) is a collaborative machine learning framework that combines on-device training and server-based aggregation. We propose an asynchronous FL design with periodic aggregation to tackle the straggler issue in FL systems. We show that an age-aware'' aggregation weighting design can significantly improve the learning performance in an asynchronous FL setting.
  arXiv  Detail & Related papers  (2022-12-14T17:33:01Z)
• Gated Recurrent Neural Networks with Weighted Time-Delay Feedback [55.596897987498174]
  We present a novel approach to modeling long-term dependencies in sequential data by introducing a gated recurrent unit (GRU) with a weighted time-delay feedback mechanism.<n>Our proposed model, named $tau$-GRU, is a discretized version of a continuous-time formulation of a recurrent unit, where the dynamics are governed by delay differential equations (DDEs)
  arXiv  Detail & Related papers  (2022-12-01T02:26:34Z)
• AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation [17.57059932879715]
  In an asynchronous learning framework, a server updates the global model once it receives an update from a client instead of waiting for all the updates to arrive as in the setting. A proposed adaptive weight aggregation algorithm, referred to as AsyncFedED, is presented.
  arXiv  Detail & Related papers  (2022-05-27T07:18:11Z)
• Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning [72.78668894576515]
  Federated Learning (FL) is a newly emerged decentralized machine learning (ML) framework. We propose an asynchronous FL framework with periodic aggregation to eliminate the straggler issue in FL systems.
  arXiv  Detail & Related papers  (2021-07-23T18:57:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.