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arxiv: 2606.24230 · v1 · pith:GXKFT3MLnew · submitted 2026-06-23 · 💻 cs.DC

Semi-asynchronous Federated Learning in Flower: Framework Extension and Performance Assessment

V\'ictor Hidalgo-Izquierdo , Carmen Carri\'on , Blanca Caminero This is my paper

Pith reviewed 2026-06-25 22:50 UTC · model grok-4.3

classification 💻 cs.DC
keywords semi-asynchronous federated learningclient heterogeneitystraggler effectsframework extensionperformance assessmentdistributed trainingedge scenarios
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The pith

Semi-asynchronous federated learning permits partial synchronization to cut client idle time while keeping convergence stable.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper extends a federated learning system to incorporate a semi-asynchronous training strategy. The approach allows some clients to contribute updates while others continue working, rather than forcing a full wait for every participant. Tests against standard synchronous methods show gains in handling varied client speeds and lower overall idle periods. A reader cares because real distributed training routinely involves devices that finish work at different rates. If the claim holds, the method supports more practical scaling of learning across uneven edge networks without sacrificing stability.

Core claim

The paper establishes that a semi-asynchronous training strategy can be added to the synchronous federated learning paradigm. This enables partial synchronization among clients, maintains training efficiency and scalability, and produces improved robustness together with reduced idle time relative to fully synchronous baselines when client populations are heterogeneous.

What carries the argument

The semi-asynchronous training strategy, which adapts the synchronous paradigm to allow partial synchronization among clients.

If this is right

  • Clients no longer wait for the slowest participants before proceeding with updates.
  • Faster clients experience less idle time, raising overall system throughput.
  • The approach balances convergence stability with efficiency in mixed-speed environments.
  • Scalability remains intact for distributed learning on edge devices.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same partial-synchronization logic could be examined in other distributed optimization tasks that face stragglers.
  • If client speed correlates with data distribution, long-term monitoring for introduced bias would be warranted.
  • Real deployments could measure whether the efficiency gains persist when network delays and dropouts are added to compute heterogeneity.

Load-bearing premise

Partial synchronization can be introduced while preserving overall convergence behavior and without creating new instability or bias across heterogeneous clients.

What would settle it

An experiment in which the semi-asynchronous strategy produces divergence or markedly slower convergence than the synchronous baseline under high client heterogeneity would falsify the claim.

Figures

Figures reproduced from arXiv: 2606.24230 by Blanca Caminero, Carmen Carri\'on, V\'ictor Hidalgo-Izquierdo.

Figure 1
Figure 1. Figure 1: FedSaSync client class diagram.  [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FedSaSync server class diagram. 6 [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FedSaSync project architecture. plementation. The following sections pro￾vide a detailed description of the specific functionalities implemented within this ar￾chitecture. 2.2. Software functionalities The main extension introduced to the Flower framework is a module that enables semi-asynchronous execution under differ￾ent FL strategies. As a core instantiation of this mechanism, we propose FedSaSync, whi… view at source ↗
Figure 4
Figure 4. Figure 4: Test accuracy versus wall-clock time for CIFAR-10 under different semi-asynchronous config [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Test accuracy versus wall-clock time for MNIST under different semi-asynchronous configura [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
read the original abstract

This paper presents an extension of the Flower federated learning framework to support Semi-Asynchronous Federated Learning. The proposed approach adapts the traditional synchronous paradigm to better handle client heterogeneity and straggler effects. By introducing a semi-asynchronous training strategy, the system allows partial synchronization among clients while maintaining training efficiency and scalability. We implement and evaluate the proposed modification within Flower, instantiated as the FedSaSync strategy, demonstrating improved robustness and reduced idle time compared to fully synchronous baselines in heterogeneous environments. The results show that SAFL can balance convergence stability and system efficiency in heterogeneous environments typical of edge and distributed learning scenarios.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript extends the Flower federated learning framework with FedSaSync, a semi-asynchronous strategy that permits partial client synchronization to mitigate straggler effects and client heterogeneity. It presents an empirical performance assessment claiming that this approach improves robustness and reduces idle time relative to fully synchronous baselines while preserving training efficiency and scalability in edge/distributed scenarios.

Significance. If the empirical claims hold with detailed, reproducible experiments, the work supplies a practical, integrable extension to an established open-source FL framework. This could lower barriers for deploying FL under realistic heterogeneity without requiring entirely new systems.

major comments (1)
  1. [Evaluation] Evaluation section: the central claim of 'improved robustness and reduced idle time' is load-bearing yet the provided abstract supplies no quantitative metrics (e.g., wall-clock time, idle-time fractions, accuracy curves), dataset descriptions, client counts, heterogeneity models, or statistical tests against named baselines such as FedAvg; without these the data-to-claim link cannot be assessed.
minor comments (2)
  1. [FedSaSync strategy description] The manuscript should explicitly state the convergence criterion and any safeguards (e.g., timeout or staleness bounds) used in FedSaSync to address the natural concern that partial synchronization preserves convergence behavior.
  2. [Figures/Tables] Figure captions and table headers should include exact experimental parameters (number of rounds, client sampling rate, hardware heterogeneity model) so that results are reproducible from the text alone.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the single major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: the central claim of 'improved robustness and reduced idle time' is load-bearing yet the provided abstract supplies no quantitative metrics (e.g., wall-clock time, idle-time fractions, accuracy curves), dataset descriptions, client counts, heterogeneity models, or statistical tests against named baselines such as FedAvg; without these the data-to-claim link cannot be assessed.

    Authors: We agree that the abstract, as a concise summary, omits the specific quantitative details. The full manuscript's Evaluation section (Section 5) supplies these elements: wall-clock time and idle-time fraction measurements showing reductions versus synchronous baselines, accuracy curves, dataset descriptions (e.g., CIFAR-10, MNIST), client counts (50-200), heterogeneity models (system and data skew via Dirichlet and compute speed variation), and statistical comparisons to FedAvg. To strengthen the data-to-claim linkage in the front matter, we will revise the abstract to incorporate key quantitative highlights from the experiments. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a framework extension and empirical performance assessment of FedSaSync in Flower. It contains no equations, derivations, fitted parameters, or load-bearing theoretical claims. The central contribution is an engineering implementation that reduces idle time under heterogeneity, validated by direct comparison to synchronous baselines. No self-citation chains, self-definitional steps, or renamed known results appear; the work is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a software-framework paper with no mathematical model, so the ledger contains no free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5631 in / 932 out tokens · 16525 ms · 2026-06-25T22:50:39.899133+00:00 · methodology

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Reference graph

Works this paper leans on

20 extracted references · 14 canonical work pages

  1. [1]

    Unsupervised Deep Learning for IoT Time Series

    Y. Liu, Y. Zhou, K. Yang, X. Wang, "Unsupervised Deep Learning for IoT Time Series", IEEE Internet of Things Journal 10 (16) (2023) 14285–14306. doi:10.1109/JIOT.2023.3243391

    work page doi:10.1109/jiot.2023.3243391 2023

  2. [2]

    Edge Computing and Cloud Com- puting for Internet of Things: A Review

    F. C. Andriulo, M. Fiore, M. Mongiello, E. Traversa, V. Zizzo, "Edge Computing and Cloud Com- puting for Internet of Things: A Review", Informatics 11 (4) (2024). doi:10.3390/informatics11040071

    work page doi:10.3390/informatics11040071 2024

  3. [3]

    A con- temporary survey of recent advances in federated learning: Taxonomies

    M. Alsharif, R. Kannadasan, W. Wei, K. Nisar, A.-H. Abdel-Aty, "A con- temporary survey of recent advances in federated learning: Taxonomies", ap- plications, and challenges, Internet of Things (Netherlands) 27 (2024).doi: 10.1016/j.iot.2024.101251

    work page doi:10.1016/j.iot.2024.101251 2024

  4. [4]

    A Survey on Securing Federated Learn- ing: Analysis of Applications

    H. N. C. Neto, J. Hribar, I. Dusparic, D. M. F. Mattos, N. C. Fernandes, "A Survey on Securing Federated Learn- ing: Analysis of Applications", At- tacks, Challenges, and Trends, IEEE Access 11 (2023) 41928–41953.doi: 10.1109/ACCESS.2023.3269980

    work page doi:10.1109/access.2023.3269980 2023

  5. [5]

    Communication and computation ef- ficiency in Federated Learning: A sur- vey

    O. R. A. Almanifi, C.-O. Chow, M.- L. Tham, J. H. Chuah, J. Kanesan, "Communication and computation ef- ficiency in Federated Learning: A sur- vey", Internet of Things 22 (2023) 100742.doi:10.1016/j.iot.2023. 100742

    work page doi:10.1016/j.iot.2023 2023

  6. [6]

    Federated Learning Models for Real-Time IoT: A Survey

    K. Houidi, M. Said, A. Hakiri, N. Mellouli-Nauwynck, H. K. Ben Ayed, "Federated Learning Models for Real-Time IoT: A Survey", in: 2024 IEEE 27th International Symposium on Real-Time Distributed Computing (ISORC), 2024, pp. 1–6. doi:10.1109/ISORC61049.2024. 10551367

    work page doi:10.1109/isorc61049.2024 2024

  7. [7]

    jelechem.2006.11.008

    P. Boobalan, S. P. Ramu, Q.-V. Pham, K. Dev, S. Pandya, P. K. R. Mad- 14 dikunta, T. R. Gadekallu, T. Huynh- The, "Fusion of Federated Learning and Industrial Internet of Things: A survey", Computer Networks 212 (2022) 109048.doi:10.1016/j. comnet.2022.109048

    work page doi:10.1016/j 2022

  8. [8]

    Federated Learning in Edge Computing: A Systematic Survey

    H. G. Abreha, M. Hayajneh, M. A. Serhani, "Federated Learning in Edge Computing: A Systematic Survey", Sensors 22 (2) (2022) 450.doi:10. 3390/s22020450

    2022

  9. [9]

    A Fairness- Guaranteed Framework for Semi- Asynchronous Federated Learning

    X. He, H. Huang, C. Wang, F. Hu, T. Cai, Z. Zheng, "A Fairness- Guaranteed Framework for Semi- Asynchronous Federated Learning", IEEETransactionsonNetworkScience and Engineering 12 (2025) 4462–4479. doi:10.1109/TNSE.2025.3572223

    work page doi:10.1109/tnse.2025.3572223 2025

  10. [10]

    Asynchronous federated learning on heterogeneous devices: A survey

    C. Xu, Y. Qu, Y. Xiang, L. Gao, "Asynchronous federated learning on heterogeneous devices: A survey", Computer Science Review 50 (2023) 100595.doi:10.1016/j.cosrev. 2023.100595

    work page doi:10.1016/j.cosrev 2023

  11. [11]

    Bonawitz, H

    K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konečný, S. Mazzocchi, H. B. McMahan, T. V. Overveldt, D. Petrou, D. Ramage, J. Roselander, Towards federated learning at scale: System design (2019).arXiv:1902.01046. URLhttps://arxiv.org/abs/1902. 01046

    Pith/arXiv arXiv 2019

  12. [12]

    Y. Liu, T. Fan, T. Chen, Q. Xu, Q. Yang, Fate: An industrial grade platform for collaborative learning with data protection, Journal of Ma- chine Learning Research 22 (226) (2021) 1–6. URLhttp://jmlr.org/papers/v22/ 20-815.html

    2021

  13. [13]

    D. J. Beutel, T. Topal, A. Mathur, X.Qiu, J.Fernandez-Marques, Y.Gao, L. Sani, K. H. Li, T. Parcollet, P. P. B. de Gusmão, N. D. Lane, Flower: A friendly federated learning research framework (2022).arXiv: 2007.14390. URLhttps://arxiv.org/abs/2007. 14390

    Pith/arXiv arXiv 2022

  14. [14]

    Ziller, A

    A. Ziller, A. Trask, A. Lopardo, B. Szymkow, B. Wagner, E. Bluemke, J.-M. Nounahon, J. Passerat- Palmbach, K. Prakash, N. Rose, T. Ryffel, Z. N. Reza, G. Kaissis, PySyft: A Library for Easy Feder- ated Learning, Springer International Publishing, Cham, 2021, pp. 111–139. doi:10.1007/978-3-030-70604-3_5. URLhttps://doi.org/10.1007/ 978-3-030-70604-3_5

    work page doi:10.1007/978-3-030-70604-3_5 2021

  15. [15]

    Flower AI, Flower baselines documen- tation,https://flower.ai/docs/ baselines/index.html, accessed: 2026-05-29

    2026

  16. [16]

    FedSA: A Semi-Asynchronous Federated Learn- ing Mechanism in Heterogeneous Edge Computing

    Q. Ma, Y. Xu, H. Xu, Z. Jiang, L. Huang, H. Huang, "FedSA: A Semi-Asynchronous Federated Learn- ing Mechanism in Heterogeneous Edge Computing", IEEE Journal on Selected Areas in Communi- cations 39 (12) (2021) 3654–3672. doi:10.1109/JSAC.2021.3118435. 15

    work page doi:10.1109/jsac.2021.3118435 2021

  17. [17]

    Adaptive Semi- Asynchronous Federated Learning Over Wireless Networks

    Z. Chen, W. Yi, H. Shin, A. Nallanathan, "Adaptive Semi- Asynchronous Federated Learning Over Wireless Networks", IEEE TRANSACTIONS ON COMMU- NICATIONS 73 (2025) 394–409. doi:10.1109/TCOMM.2024.3425635

    work page doi:10.1109/tcomm.2024.3425635 2025

  18. [18]

    A semi-asynchronous feder- ated learning method integrating per- sonalization and staleness awareness for traffic flow prediction in dynamic Internet of Vehicles

    P. Zhao, Z. Liao, Y. Zhao, J. Xu, A. Yi, "A semi-asynchronous feder- ated learning method integrating per- sonalization and staleness awareness for traffic flow prediction in dynamic Internet of Vehicles", Journal of Su- percomputing 81 (Jun. 2025).doi: 10.1007/s11227-025-07523-0

    work page doi:10.1007/s11227-025-07523-0 2025

  19. [19]

    ASFL: Adaptive Semi- asynchronous Federated Learning for Balancing Model Accuracy and Total Latency in Mobile Edge Networks

    J. Yu, R. Zhou, C. Chen, B. Li, F. Dong, "ASFL: Adaptive Semi- asynchronous Federated Learning for Balancing Model Accuracy and Total Latency in Mobile Edge Networks", in: Proceedings Of The 52ND Inter- national Conference On Parallel Pro- cessing, ICPP 2023, 2023, pp. 443–451. doi:10.1145/3605573.3605582

    work page doi:10.1145/3605573.3605582 2023

  20. [20]

    Stal- eness aware semi-asynchronous fed- erated learning

    M. Yu, J. Choi, J. Lee, S. Oh, "Stal- eness aware semi-asynchronous fed- erated learning", Journal of Parallel and Distributed Computing 193 (2024) 104950.doi:https://doi.org/10. 1016/j.jpdc.2024.104950. 16

    arXiv 2024