arxiv: 2604.26388 · v1 · submitted 2026-04-29 · 💻 cs.DC · cs.LG

Recognition: unknown

SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning

Yimeng Shan , Zhaorui Zhang , Sheng Di , Yu Liu , Xiaoyi Lu , Benben Liu

Authors on Pith no claims yet

Pith reviewed 2026-05-07 10:58 UTC · model grok-4.3

classification 💻 cs.DC cs.LG

keywords federated learningsplit learningLLM fine-tuningadaptive cut layerLoRAcommunication efficiencydata heterogeneity

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The pith

SplitFT lets clients choose cut layers by resources and trims LoRA rank at the split to speed federated LLM fine-tuning

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

The paper introduces SplitFT, a system that lets each client in a federated split learning setup pick its own cut layer according to available compute and current model quality. This addresses heterogeneity in devices and data when fine-tuning large language models. To lower communication costs, SplitFT reduces the LoRA rank specifically at the chosen cut layer. A new length-based Dirichlet method splits the training data to mimic real-world distributions across clients. Tests on standard benchmarks show faster fine-tuning times and better final model performance than previous methods.

Core claim

SplitFT is an adaptive federated split learning system for LLMs fine-tuning where clients set different cut layers based on their computation resources and trained model performance, and LoRA rank is reduced in the cut layer to decrease communication overhead, with a length-based Dirichlet approach for data division, leading to improved fine-tuning time efficiency and model performance.

What carries the argument

Adaptive selection of cut layers by clients according to local resources and performance, paired with LoRA rank reduction at the cut layer to minimize communication.

If this is right

Clients with limited resources can still participate effectively by choosing shallower cut layers.
Overall system fine-tuning completes faster while achieving higher model accuracy on benchmarks.
Communication volume drops due to lower LoRA rank without harming convergence.
Heterogeneous data distributions are handled better through the proposed partitioning method.
The approach scales to various popular LLM benchmarks without additional coordination overhead.

Where Pith is reading between the lines

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

The method might allow integration with dynamic cut-layer changes during training if performance monitoring is added.
Similar adaptations could apply to other split learning scenarios beyond LLMs, such as vision models.
The length-based Dirichlet division could be compared to standard Dirichlet for generalizability in other federated settings.
Reducing rank only at cut might preserve more model capacity if applied selectively.

Load-bearing premise

Clients can select different cut layers based on local resources and model performance without introducing coordination overhead, instability in convergence, or degradation in final model quality.

What would settle it

Running the system with fixed cut layers versus adaptive selection in a highly heterogeneous client setup and observing no improvement or degradation in time or performance would falsify the benefit of adaptation.

Figures

Figures reproduced from arXiv: 2604.26388 by Benben Liu, Sheng Di, Xiaoyi Lu, Yimeng Shan, Yu Liu, Zhaorui Zhang.

**Figure 1.** Figure 1: The System Overview of Our Proposed SplitFT. number of samples allocated to client i from category k is nki = ⌊pki · nk⌋. Using these values, samples from Dk are randomly selected and allocated to the respective clients. Each client’s local dataset is then formed by combining the allocated samples from all categories: Dc,i = SK k=1 Dki, where Dki represents the subset of samples from category k assigned to… view at source ↗

**Figure 2.** Figure 2: The Impact of LoRA Rank and Cutlayer on Model Performance and Quality. view at source ↗

**Figure 3.** Figure 3: The Performance Comparison for SplitFT and Baselines. TABLE I: Comparison for Accuracy, Elapsed Time, Round Time, and Communication Overhead for Different Cutlayers. Cutlayer Max Accuracy Mean Elapsed Time (s) Mean Round Time (s) Max Comm Overhead (MB) 2 0.0606 810.4379 0.0347 3475.3674 4 0.0571 863.2450 0.0424 3534.3875 6 0.0605 934.1831 0.0547 3593.4076 8 0.0621 1113.3617 0.0635 3652.4278 10 0.0629 1104.… view at source ↗

**Figure 4.** Figure 4: Generalizability of SplitFT Across Different Models view at source ↗

read the original abstract

Federated Split Learning has been identified as an efficient approach to address the computational resource constraints of clients in classical federated learning, while guaranteeing data privacy for distributed model training across data owners. However, it faces some critical challenges when such a training strategy meets large language models (LLMs) for fine-tuning. Such challenges include setting the cutlayer adaptively across different clients to address the data and device heterogeneity issues, which affect the system performance significantly. In addition, efficiently reducing the communication overhead during the fine-tuning procedure is also another challenge. No work tries to address these challenges. To bridge this gap, we propose SplitTF, an adaptive federated split learning system for LLMs fine-tuning. SplitFT enables different clients to set different cut layers according to their computation resources and trained model performance. SplitFT also proposes to reduce the LoRA rank in cutlayer to reduce the communication overhead. In addition to simulating the heterogeneous data in real-world applications for our proposed split federated learning system, we propose a length-based Dirichlet approach to divide the training data into different clients. Extensive experimental results show that our proposed approach outperforms the state-of-the-art approach for fine-tuning time efficiency and model performance based on various popular benchmarks.

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

SplitFT adapts cut layers per client and shrinks LoRA rank at the cut for federated LLM fine-tuning, which targets real heterogeneity, but the server-side handling of mismatched model segments is the part that still needs explicit validation.

read the letter

SplitFT's main move is letting each client pick its own cut layer based on local resources and how well the model is training there, then dropping the LoRA rank at that cut to shrink the activations and gradients that get sent. They also use a length-based Dirichlet split to create more realistic heterogeneous data partitions across clients. That specific pairing of adaptive cuts and rank reduction at the boundary has not been done before in federated split learning for LLMs, so the design itself is the fresh element worth noting.

Referee Report

2 major / 1 minor

Summary. The paper proposes SplitFT (noted as SplitTF in one place), an adaptive federated split learning system for fine-tuning LLMs. Clients independently select different cut layers according to local compute resources and model performance to address heterogeneity; the system reduces LoRA rank specifically at the cut layer to lower communication cost; a length-based Dirichlet method is introduced to partition training data heterogeneously across clients; and the authors claim that extensive experiments show outperformance versus SOTA in both fine-tuning time efficiency and final model quality on popular benchmarks.

Significance. If the adaptive cut-layer mechanism and server-side aggregation can be shown to preserve convergence and model quality under realistic heterogeneity, the work would be a meaningful step toward practical federated LLM fine-tuning on resource-constrained devices while preserving privacy. The length-based Dirichlet partitioning is a concrete, reusable contribution for simulating non-IID data in split-learning studies.

major comments (2)

[Abstract] Abstract: the central claim that 'extensive experimental results show that our proposed approach outperforms the state-of-the-art approach for fine-tuning time efficiency and model performance' is stated without any quantitative metrics, baselines, statistical tests, or description of how cut-layer decisions are validated. This absence makes the performance claims impossible to assess from the provided text.
[Abstract] Abstract (system description): SplitFT lets each client choose a different cut layer, yet no mechanism is described for aligning activations/gradients or performing partial aggregation when clients operate on mismatched model segments. Because the cut layer determines exactly which parameters are updated locally versus on the server, heterogeneous choices directly affect the global model update; without an explicit reconciliation strategy, the reported gains in convergence speed and final quality rest on an unstated assumption that such heterogeneity introduces neither instability nor quality loss.

minor comments (1)

[Abstract] Abstract: the system name is introduced as 'SplitTF' and then used as 'SplitFT'; this inconsistency should be corrected for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments on our paper. We have addressed each of the major comments point by point below and made revisions to the manuscript where necessary to improve clarity and completeness.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that 'extensive experimental results show that our proposed approach outperforms the state-of-the-art approach for fine-tuning time efficiency and model performance' is stated without any quantitative metrics, baselines, statistical tests, or description of how cut-layer decisions are validated. This absence makes the performance claims impossible to assess from the provided text.

Authors: We agree that the abstract, as a concise summary, does not include specific quantitative metrics or details on validation. The full experimental results, including quantitative comparisons to state-of-the-art methods, time efficiency gains, model performance on benchmarks, and the process for cut-layer selection and validation, are presented in detail in Sections 4 and 5 of the manuscript. To address this concern, we have revised the abstract to incorporate key quantitative highlights from our experiments and a brief mention of the cut-layer decision validation, while maintaining its brevity. This revision makes the claims more assessable directly from the abstract. revision: yes
Referee: [Abstract] Abstract (system description): SplitFT lets each client choose a different cut layer, yet no mechanism is described for aligning activations/gradients or performing partial aggregation when clients operate on mismatched model segments. Because the cut layer determines exactly which parameters are updated locally versus on the server, heterogeneous choices directly affect the global model update; without an explicit reconciliation strategy, the reported gains in convergence speed and final quality rest on an unstated assumption that such heterogeneity introduces neither instability nor quality loss.

Authors: We thank the referee for highlighting this important aspect. Upon review, we realize that while the mechanism is implemented in our system (as the experiments demonstrate convergence), the description in the abstract is insufficient. We have revised the abstract to briefly outline the alignment process: clients send activations to the server at their cut layer, the server handles the remaining computation and aggregates server-side LoRA updates. We have also added a dedicated paragraph in Section 3 explaining the partial aggregation and how it preserves model consistency and convergence under heterogeneous cut layers, including analysis showing no significant instability in our experiments. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical system design with no derivations or fitted predictions

full rationale

The paper describes a system architecture (SplitFT) for adaptive cut-layer selection and LoRA rank reduction in federated split learning for LLMs, plus a length-based Dirichlet data partitioning method. All claims of outperformance are based on experimental results across benchmarks rather than any mathematical derivation chain, first-principles predictions, or parameter fitting. No equations, self-citations as load-bearing premises, ansatzes, or uniqueness theorems are invoked that could reduce to inputs by construction. The contribution is a practical design evaluated empirically, making it self-contained against external benchmarks with no circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The proposal rests on standard assumptions of federated learning (data privacy via local training) and parameter-efficient fine-tuning via LoRA. No new free parameters, axioms, or invented entities are introduced in the abstract description.

pith-pipeline@v0.9.0 · 5531 in / 1070 out tokens · 72432 ms · 2026-05-07T10:58:08.500853+00:00 · methodology

discussion (0)

Reference graph

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Client-side Model Aggregation and Interaction with Server-side Model:This subsection covers the aggregation of client-side LoRA adapter updates and their interaction with the server-side model, comprising five key steps. (b1) Client-side LoRA Adapters’ Update Transmission: After interacting with the main server, each client serverical- culates the changes...