Separate Aggregation of Split Network for Personalized Federated Learning

Jaeyoung Song; Yunseok Kang

arxiv: 2605.26571 · v1 · pith:GIOSEC3Bnew · submitted 2026-05-26 · 💻 cs.LG

Separate Aggregation of Split Network for Personalized Federated Learning

Yunseok Kang , Jaeyoung Song This is my paper

Pith reviewed 2026-06-29 18:54 UTC · model grok-4.3

classification 💻 cs.LG

keywords personalized federated learningsplit networksadaptive aggregationheterogeneous datasynthetic representationslabel imbalanceGaussian statistics

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The pith

PGFedSplit uses a split network with adaptive aggregation and synthetic representations to improve both personalization and global generalization in federated learning under severe client data heterogeneity.

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

The paper introduces PGFedSplit to handle the tradeoff in personalized federated learning where more global sharing can hurt local fit and more local adaptation can cause overfitting on small or imbalanced client data. It splits the model into parts and applies different aggregation schedules to each part so that shared knowledge stays stable while client-specific parts adapt freely. Clients then combine their own extracted features with synthetic features drawn from server-side Gaussian statistics to fill gaps when classes are missing or labels are skewed. Experiments on standard image datasets show gains over prior personalized methods in highly non-uniform settings.

Core claim

PGFedSplit adopts a split architecture and performs adaptive aggregation scheduling tailored to the roles of different model components, enabling stable knowledge sharing while maintaining client specific adaptation. Each client further leverages a mixture of locally extracted representations and synthetic representations generated from server side Gaussian statistics, improving robustness under label imbalance and missing class conditions.

What carries the argument

Split architecture with adaptive aggregation scheduling for model components plus mixture of locally extracted representations and synthetic representations from server-side Gaussian statistics.

If this is right

The method simultaneously raises client-specific accuracy and global model quality without forcing a tradeoff between them.
Separate scheduling for different model parts allows stable sharing of early layers while preserving adaptation in later layers.
Mixing local and server-generated synthetic representations reduces sensitivity to missing classes and label skew at each client.
The approach yields consistent gains on Fashion-MNIST, CIFAR-10, CIFAR-100 and Tiny-ImageNet under controlled heterogeneity.

Where Pith is reading between the lines

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

The same split-and-schedule pattern could be applied to other distributed training tasks where different layers have different privacy or stability requirements.
Generating synthetic features from summary statistics at the server may reduce communication cost compared with sharing raw data or full models.
If the Gaussian assumption holds only for image data, the technique might need different summary statistics when applied to text or tabular client data.

Load-bearing premise

Server-side Gaussian statistics can generate synthetic representations that reliably improve robustness under label imbalance and missing-class conditions without introducing new biases or harming convergence.

What would settle it

Remove the synthetic-representation step and rerun the experiments on the same highly imbalanced datasets; if the reported gains in personalization and robustness disappear or reverse, the central mechanism does not hold.

Figures

Figures reproduced from arXiv: 2605.26571 by Jaeyoung Song, Yunseok Kang.

**Figure 1.** Figure 1: shows the case where the personalization layer is aggregated at every communication round. In this regime, preserving the local classifier is critical: when α is too small, the aggregated head repeatedly overwrites local specialization and degrades performance. By contrast, [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** Figure 2: Accuracy under different α when the personalization layer is aggregated every 20 rounds. Right: zoomed final convergence. Related Works Federated Learning Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data (McMahan et al. 2017). A central challenge in FL is statistical heterogeneity across clients, which can lead to biased global updates and … view at source ↗

**Figure 3.** Figure 3: Overall PGFedSplit workflow: representation layers aggregate every round, personalization layers are synchronized [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 5.** Figure 5: Ablation study on CIFAR-100 under Dirichlet [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

read the original abstract

Federated learning enables collaborative model training without sharing raw data, but its performance can degrade substantially under heterogeneous client data distributions. A single global model often cannot satisfy diverse client requirements, so personalized federated learning has therefore been explored to improve client specific performance while preserving global generalization. Existing PFL methods often face a fundamental tradeoff in which stronger global sharing can undermine local specialization, whereas stronger local adaptation can lead to overfitting under limited data, label imbalance, and missing class scenarios. In this work, we propose PGFedSplit, a personalized federated learning framework that improves both personalization and global generalization under severe client heterogeneity. PGFedSplit adopts a split architecture and performs adaptive aggregation scheduling tailored to the roles of different model components, enabling stable knowledge sharing while maintaining client specific adaptation. Each client further leverages a mixture of locally extracted representations and synthetic representations generated from server side Gaussian statistics, improving robustness under label imbalance and missing class conditions. Extensive experiments on Fashion MNIST, CIFAR 10, CIFAR 100, and Tiny ImageNet demonstrate consistent improvements over state of the art PFL methods, with stable convergence and superior personalization in highly heterogeneous settings.

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.

Desk Editor's Note private letter to a colleague

PGFedSplit combines split models, role-specific aggregation, and Gaussian synthetic reps for PFL, but the abstract shows no numbers and the Gaussian step looks like the weakest link.

read the letter

The core of this paper is PGFedSplit: a split-network setup where aggregation is scheduled differently depending on which part of the model is being updated, plus a mix of local client features and synthetic ones drawn from server-side per-class Gaussians to handle missing classes and imbalance.

What is actually new is the particular scheduling rule tied to model-component roles and the decision to generate synthetics from Gaussian statistics collected at the server. Those two choices are not standard in the PFL literature I know, even if the ingredients (splitting, adaptive aggregation, synthetic augmentation) are familiar.

The paper does a clear job stating the practical tension in PFL—stronger sharing hurts personalization, stronger local adaptation hurts under small or incomplete client data—and then offers one concrete way to navigate it. That framing is useful.

The soft spot is the Gaussian assumption itself. Deep features on CIFAR-100 or Tiny-ImageNet are not Gaussian, and when clients miss entire classes the server statistics will be biased. Sampling from those Gaussians could easily inject distribution shift instead of robustness. The abstract claims “consistent improvements” and “stable convergence” on four datasets, yet supplies no tables, no error bars, no ablation that isolates the synthetic component, and no statistical test. Without those, the central claim stays unverified.

This is for people already working on deployed federated systems with real label skew. A reader in that niche might pick up the scheduling idea or the synthetic-mixing pattern if the experiments later check out. Right now the work is too thin on evidence to cite or build on directly.

I would send it to review if the full paper contains proper ablations and quantitative results; otherwise it needs another round of experiments first.

Referee Report

2 major / 0 minor

Summary. The manuscript proposes PGFedSplit, a personalized federated learning framework using a split network architecture with adaptive aggregation scheduling tailored to model component roles, combined with a mixture of locally extracted representations and synthetic representations generated from server-side Gaussian statistics, to improve both personalization and global generalization under severe client heterogeneity including label imbalance and missing classes. It claims consistent improvements and stable convergence over state-of-the-art PFL methods on Fashion MNIST, CIFAR-10, CIFAR-100, and Tiny ImageNet.

Significance. If the empirical claims hold with proper validation, the split-architecture approach with role-specific aggregation and Gaussian synthetic augmentation could offer a useful mechanism for mitigating the personalization-globalization tradeoff in non-IID federated settings, particularly where clients have incomplete class sets.

major comments (2)

[Abstract, §3] Abstract and method description (§3 implied): The central claim that mixing local representations with synthetic ones drawn from server-side Gaussian statistics reliably improves robustness under missing-class conditions is load-bearing, yet no ablation, statistical test of Gaussianity, or isolation of the synthetic component's effect is provided; this leaves the weakest assumption unexamined.
[Abstract] Abstract: The assertions of 'consistent improvements' and 'stable convergence' are presented without any quantitative results, tables, figures, error bars, or statistical tests, so the empirical support for the central claim cannot be evaluated from the manuscript.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments point-by-point below, indicating where revisions will be made to strengthen the manuscript.

read point-by-point responses

Referee: [Abstract, §3] Abstract and method description (§3 implied): The central claim that mixing local representations with synthetic ones drawn from server-side Gaussian statistics reliably improves robustness under missing-class conditions is load-bearing, yet no ablation, statistical test of Gaussianity, or isolation of the synthetic component's effect is provided; this leaves the weakest assumption unexamined.

Authors: We acknowledge that the current manuscript lacks an explicit ablation isolating the synthetic representation component and does not include a statistical assessment of the Gaussianity assumption. In the revised version we will add an ablation study that compares the full mixture against a local-only baseline under controlled missing-class settings on at least two datasets. We will also insert a short justification for the Gaussian model (based on the central-limit behavior of aggregated client statistics) together with a brief empirical check of feature-distribution normality on the server-side aggregates. These additions directly address the concern that the assumption remains unexamined. revision: yes
Referee: [Abstract] Abstract: The assertions of 'consistent improvements' and 'stable convergence' are presented without any quantitative results, tables, figures, error bars, or statistical tests, so the empirical support for the central claim cannot be evaluated from the manuscript.

Authors: Abstracts are deliberately concise and conventionally omit numerical tables or figures. The full manuscript already contains the supporting evidence: Section 4 reports accuracy tables with standard deviations across multiple random seeds, convergence plots, and comparative results against state-of-the-art PFL baselines on all four datasets. To improve clarity we will append a single sentence to the abstract that explicitly directs readers to Section 4 for the quantitative evaluation, without altering the abstract's length constraints. revision: partial

Circularity Check

0 steps flagged

No circularity detected; framework is a self-contained empirical proposal

full rationale

The paper introduces PGFedSplit as a split-network PFL method with adaptive aggregation and Gaussian synthetic representations, supported by experiments on Fashion-MNIST, CIFAR-10/100 and Tiny-ImageNet. No equations, derivations, or predictions are shown that reduce by construction to fitted inputs, self-citations, or renamed known results. The central claims rest on the described architecture and empirical results rather than any load-bearing self-referential step, satisfying the criteria for a non-circular derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; full manuscript would be required to audit modeling choices such as aggregation weights or Gaussian assumptions.

pith-pipeline@v0.9.1-grok · 5725 in / 1067 out tokens · 26709 ms · 2026-06-29T18:54:56.132226+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

3 extracted references · 1 canonical work pages

[1]

InInternational Conference on Computer Vision, 345–354

Network Dissection: Quantifying Interpretability of Deep Visual Representations. InInternational Conference on Computer Vision, 345–354. IEEE. Bengio, Y .; Courville, A.; and Vincent, P. 2013. Represen- tation learning: A review and new perspectives.IEEE trans- actions on pattern analysis and machine intelligence, 35(8): 1798–1828. Collins, L.; Hassani, H...

2013
[2]

InInternational conference on machine learning, 2089–2099

Exploiting shared representations for personalized federated learning. InInternational conference on machine learning, 2089–2099. PMLR. Dai, Y .; Chen, Z.; Li, J.; Heinecke, S.; Sun, L.; and Xu, R

2089
[3]

arXiv preprint arXiv:2003.13461 , year=

Tackling Data Heterogeneity in Federated Learning with Class Prototypes. InProceedings of the AAAI Confer- ence on Artificial Intelligence, volume 37, 7314–7322. Deng, Y .; Kamani, M.; and Mahdavi, M. 2020. Adap- tive Personalized Federated Learning.arXiv preprint arXiv:2003.13461. Dorszewski, T.; Tˇetkov´a, L.; Jenssen, R.; Hansen, L. K.; and Wickstrøm, ...

work page arXiv 2020

[1] [1]

InInternational Conference on Computer Vision, 345–354

Network Dissection: Quantifying Interpretability of Deep Visual Representations. InInternational Conference on Computer Vision, 345–354. IEEE. Bengio, Y .; Courville, A.; and Vincent, P. 2013. Represen- tation learning: A review and new perspectives.IEEE trans- actions on pattern analysis and machine intelligence, 35(8): 1798–1828. Collins, L.; Hassani, H...

2013

[2] [2]

InInternational conference on machine learning, 2089–2099

Exploiting shared representations for personalized federated learning. InInternational conference on machine learning, 2089–2099. PMLR. Dai, Y .; Chen, Z.; Li, J.; Heinecke, S.; Sun, L.; and Xu, R

2089

[3] [3]

arXiv preprint arXiv:2003.13461 , year=

Tackling Data Heterogeneity in Federated Learning with Class Prototypes. InProceedings of the AAAI Confer- ence on Artificial Intelligence, volume 37, 7314–7322. Deng, Y .; Kamani, M.; and Mahdavi, M. 2020. Adap- tive Personalized Federated Learning.arXiv preprint arXiv:2003.13461. Dorszewski, T.; Tˇetkov´a, L.; Jenssen, R.; Hansen, L. K.; and Wickstrøm, ...

work page arXiv 2020