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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!
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 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.
AbstractList 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.
Author Tziampazis, Stefanos
Weyrich, Michael
Gul, Baran Can
Jazdi, Nasser
Author_xml – sequence: 1
  givenname: Baran Can
  surname: Gul
  fullname: Gul, Baran Can
  email: baran-can.guel@ias.uni-stuttgart.de
  organization: University of Stuttgart,Institute of Industrial Automation and Software Engineering,Germany
– sequence: 2
  givenname: Stefanos
  surname: Tziampazis
  fullname: Tziampazis, Stefanos
  email: stefanos.tziampazis@gsame.uni-stuttgart.de
  organization: University of Stuttgart,Institute of Industrial Automation and Software Engineering,Germany
– sequence: 3
  givenname: Nasser
  surname: Jazdi
  fullname: Jazdi, Nasser
  email: nasser.jazdi@ias.uni-stuttgart.de
  organization: University of Stuttgart,Institute of Industrial Automation and Software Engineering,Germany
– sequence: 4
  givenname: Michael
  surname: Weyrich
  fullname: Weyrich, Michael
  email: michael.weyrich@ias.uni-stuttgart.de
  organization: University of Stuttgart,Institute of Industrial Automation and Software Engineering,Germany
BookMark eNo1UMtOg0AUHY0m1sofmMgPUOfBvNyRBtSExEVZuGsGuNOOKQMZMFq_XupjdXKei3ONLnzvAaE7gleEYH2fV0UmOCdqRTHls0Yxl1KcoUhLrRgjnDPF1DlaEJ2KBEuur1A0jm8Y47kvNNML9Lo5-qaA9iGuXAdJ9mECxDOHYCZo4xJM8M7v4mkf-vfdPs4_h4Nr3PQTHyfTDSfX-DY-Dc0h777M5Hp_gy6tOYwQ_eESbYq8Wj8l5cvj8zorE6fZlACmVhBqU1CUNppbIZkEXRNrVc1YS6yhCojiquGkqQkWCrAFLC2zLU_ZEt3-rjoA2A7BdSYct_9XsG9flVYI
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ETFA65518.2025.11205776
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9798331553838
EISSN 1946-0759
EndPage 8
ExternalDocumentID 11205776
Genre orig-research
GroupedDBID 6IE
6IF
6IK
6IL
6IN
AAJGR
AAWTH
ABLEC
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
IPLJI
OCL
RIE
RIL
RNS
ID FETCH-LOGICAL-i93t-e02f612f4e822c95f6737e9b1ff8b33d1fa28e1858c51cb1068e0fe07f3fd543
IEDL.DBID RIE
IngestDate Wed Oct 29 06:13:06 EDT 2025
IsPeerReviewed false
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i93t-e02f612f4e822c95f6737e9b1ff8b33d1fa28e1858c51cb1068e0fe07f3fd543
PageCount 8
ParticipantIDs ieee_primary_11205776
PublicationCentury 2000
PublicationDate 2025-Sept.-9
PublicationDateYYYYMMDD 2025-09-09
PublicationDate_xml – month: 09
  year: 2025
  text: 2025-Sept.-9
  day: 09
PublicationDecade 2020
PublicationTitle Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation)
PublicationTitleAbbrev ETFA
PublicationYear 2025
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0001096939
Score 2.316173
Snippet As Federated Learning (FL) expands to larger and more distributed environments, consistency in training is challenged by network-induced delays, clock...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Delays
Distributed Training
Federated learning
Manufacturing automation
Network Time Protocol
Numerical models
Protocols
Reliability
Semantics
Servers
Synchronization
Temporal Consistency
Timestamp-based Aggregation
Training
Title SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronization
URI https://ieeexplore.ieee.org/document/11205776
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV27TsNAEFyRiIIKEEG8dQWtE9vn19FFKBYFiiIlRbroHnvIBQ4KDoi_Z-_sEFFQ0NmWbVl7xcycd2YB7kVoSexgFigVJyRQTBIIoUi4GkUIYayJ0EfmP-fTabFcillnVvdeGET0zWc4dIf-X75Z663bKhsRNyB6kWc96OV53pq19hsqRMYFF10PF52NJotynLnEMZKBcTrcPf1rjoqHkfL4nx9wAoO9IY_NfqDmFA6wPoPl_KvWJZoH5nwcwfhTbpCVLhyC-KNhXXLqC-tG8TDXblfpqvG3Eyl8dVYpJmvD3It8SG7ryRzAvJwsHp-CblBCUAneBBjGloiKTZDQXovUutkzKFRkbaE4N5GVcYEEzIVOI61IBBYYWgxzy61JE34O_Xpd4wWwIiE6EgokGHfjwaXUOou0JI4TIpdWX8LAFWX11iZhrHb1uPrj-jUcudL7nixxA_1ms8VbONQfTfW-ufPr9w3ILJxu
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwFLSgIMEEiCK-8cCaNomdNGarUKMiSlWpHbpV_nhGGUirkoL49zy7KRUDA1scJVZkD3fPeXdHyL0ILRY7kAZKxRwLFMMDIRQWrkYhQhhrIvCW-YPOcJhNp2JUi9W9FgYAfPMZtNyl_5dv5nrljsrayA2QXnTSXbKXcB5Ha7nW9kgF6bhgou7iwlG7N8m7qfMcw0IwTlqb938lqXggyY_--QnHpLmV5NHRD9ickB0oT8l0_FXqHMwDdUqOoPspl0BzZw-BDNLQ2jv1ldZhPNQ13BW6qPzjSAvfnFiKytJQN5G3yV2rMptknPcmj_2gjkoICsGqAMLYIlWxHBDvtUisS58BoSJrM8WYiayMM0BoznQSaYVlYAahhbBjmTUJZ2ekUc5LOCc040hIQgEI5C4gXEqt00hLZDkhMGn1BWm6RZkt1l4Ys816XP5x_44c9Ccvg9ngafh8RQ7dNvgOLXFNGtVyBTdkX39Uxfvy1u_lNxp3n7U
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Proceedings+%28IEEE+International+Conference+on+Emerging+Technologies+and+Factory+Automation%29&rft.atitle=SyncFed%3A+Time-Aware+Federated+Learning+through+Explicit+Timestamping+and+Synchronization&rft.au=Gul%2C+Baran+Can&rft.au=Tziampazis%2C+Stefanos&rft.au=Jazdi%2C+Nasser&rft.au=Weyrich%2C+Michael&rft.date=2025-09-09&rft.pub=IEEE&rft.eissn=1946-0759&rft.spage=1&rft.epage=8&rft_id=info:doi/10.1109%2FETFA65518.2025.11205776&rft.externalDocID=11205776