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arxiv: 2504.09114 · v1 · submitted 2025-04-12 · 💻 cs.LG

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning

Xianke Qiang , Hongda Liu , Xinran Zhang , Zheng Chang , Ying-Chang Liang This is my paper

Pith reviewed 2026-05-22 21:03 UTC · model grok-4.3

classification 💻 cs.LG
keywords split federated learninglarge AI modelsedge computingquantizationresource allocationfederated fine-tuningmodel partitioninglatency-energy trade-off
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The pith

Split federated learning with quantization enables large AI models to train on memory-limited edge devices by partitioning layers and optimizing resources.

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

The paper proposes SFLAM, a framework that partitions large AI model training between edge devices and servers via split learning to cut on-device memory needs. It adds quantization of model parts along with power control and bandwidth allocation to lower energy use and communication delays while keeping training feasible. A latency-energy trade-off analysis supports the design, and simulations compare it against conventional federated and split methods. The result is reported as higher learning efficiency and better scalability for deploying advanced models under device constraints.

Core claim

The paper claims that the Quantized Split Federated Fine-Tuning Large AI Model (SFLAM) framework, which splits model layers across devices and servers and jointly manages quantization, transmit power, and bandwidth, allows fine-tuning of large models on resource-limited mobile edge devices while achieving superior learning efficiency and scalability over standard approaches.

What carries the argument

The split learning paradigm that divides model layers between edge devices and servers, combined with quantization management and joint power-bandwidth allocation.

If this is right

  • Cuts memory footprint on each edge device enough to run models that would otherwise exceed device limits.
  • Lowers total energy draw and communication rounds during training.
  • Supports more devices participating simultaneously without proportional increases in overhead.
  • Provides a theoretical bound on the latency-energy trade-off that guides allocation choices.

Where Pith is reading between the lines

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

  • The same partitioning idea could apply to inference-only serving on heterogeneous device fleets.
  • Dynamic layer splitting that changes with device battery or network state might further improve results.
  • Integration with differential privacy mechanisms could strengthen the privacy guarantees already implied by keeping raw data local.

Load-bearing premise

Splitting the model and applying quantization plus resource allocation keeps model accuracy and convergence rates close to those of full centralized training.

What would settle it

A direct comparison on a transformer with billions of parameters showing final accuracy drops more than a few percent or convergence slows dramatically under SFLAM versus server-only training.

Figures

Figures reproduced from arXiv: 2504.09114 by Hongda Liu, Xianke Qiang, Xinran Zhang, Ying-Chang Liang, Zheng Chang.

Figure 1
Figure 1. Figure 1: The architecture of LAMs. particularly in sensitive domains such as healthcare [3] and finance [4], often restrict direct data sharing. This necessitates collaborative training methodologies that leverage distributed data while safeguarding privacy. Federated Learning (FL) offers a solution by enabling multiple data owners to col￾laboratively fine-tune LAMs without exchanging raw data [5]–[7]. Nevertheless… view at source ↗
Figure 2
Figure 2. Figure 2: The framework of SFLAM. vision transformer and introduces a block sampling module. A recent U-shaped SFL framework [23] employs semantic-aware auto-encoders and reinforcement learning to enhance privacy, communication, and performance in vehicular networks. While these approaches enable collaborative fine-tuning across distributed devices while reducing computational and communication costs, they still dem… view at source ↗
Figure 3
Figure 3. Figure 3: Testing accuracy with random selection of 10 out of 50 devices under different Dirichlet distributions. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Average energy consumption and objective value after solving three subproblems under different [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Time and Energy Consumption • Equal uplink transmit power (EP): With the uplink power fixed (1.5W), we focus solely on optimizing the wireless resource allocation and quantization management. • Radom bandwidth allocation (RB): At each round, ran￾domly selected devices are assigned subchannels with optimal power control and quantization management. • No quantization management (NQ): The scheme is without qu… view at source ↗
Figure 6
Figure 6. Figure 6: Testing accuracy on Vit-Base/32 Model intermediate parameters, such as activations and gradients, which are positively correlated with the amount of data. By incorporating quantization management, the proposed method significantly reduces both communication time and energy [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
read the original abstract

Large Artificial Intelligence Models (LAMs) powered by massive datasets, extensive parameter scales, and extensive computational resources, leading to significant transformations across various industries. Yet, their practical deployment on resource-limited mobile edge devices is hindered by critical challenges such as data privacy, constrained resources, and high overhead costs. Addressing this gap, this paper proposes a novel framework, named Quantized Split Federated Fine-Tuning Large AI Model (SFLAM). By partitioning the training load between edge devices and servers using a split learning paradigm, SFLAM can facilitate the operation of large models on devices and significantly lowers the memory requirements on edge devices. Additionally, SFLAM incorporates quantization management, power control, and bandwidth allocation strategies to enhance training efficiency while concurrently reducing energy consumption and communication latency. A theoretical analysis exploring the latency-energy trade-off is presented, and the framework's efficacy is validated via comprehensive simulations. The findings indicate that SFLAM achieves superior performance in terms of learning efficiency and scalability compared to conventional methods, thereby providing a valuable approach for enabling advanced AI services in resource-constrained 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

2 major / 1 minor

Summary. This paper proposes the Quantized Split Federated Fine-Tuning Large AI Model (SFLAM) framework to deploy large AI models on resource-limited devices. It partitions training via split learning to lower edge-device memory needs, incorporates quantization management along with power control and bandwidth allocation to cut energy and latency, presents a theoretical latency-energy trade-off analysis, and validates the approach through simulations that reportedly show superior learning efficiency and scalability versus conventional methods.

Significance. If the theoretical analysis and simulation results hold under scrutiny, the work could meaningfully advance practical edge deployment of large models by jointly addressing privacy, memory, energy, and communication constraints. It extends split and federated learning with resource-aware optimizations, offering a potentially useful template for resource-constrained AI services.

major comments (2)
  1. Abstract: The manuscript asserts that a theoretical latency-energy trade-off analysis is presented and that efficacy is validated via comprehensive simulations, yet the provided text contains no equations, derivations, simulation parameters, baselines, metrics, or error bars. These omissions are load-bearing for the central claim of superior performance and prevent verification of the reported efficiency and scalability gains.
  2. Theoretical Analysis section: No specific formulation of the latency-energy trade-off (e.g., expressions relating quantization bits, power, bandwidth, and convergence) is visible, making it impossible to assess whether the analysis is rigorous or merely descriptive.
minor comments (1)
  1. Abstract: The opening sentence is a grammatical fragment ('Large Artificial Intelligence Models (LAMs) powered by massive datasets... leading to...'). It should be rewritten as a complete sentence.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the recommendation for major revision. We address each major comment point by point below and commit to substantial revisions that add the requested explicit details without altering the core contributions.

read point-by-point responses

  1. Referee: Abstract: The manuscript asserts that a theoretical latency-energy trade-off analysis is presented and that efficacy is validated via comprehensive simulations, yet the provided text contains no equations, derivations, simulation parameters, baselines, metrics, or error bars. These omissions are load-bearing for the central claim of superior performance and prevent verification of the reported efficiency and scalability gains.

    Authors: We acknowledge that the abstract and main text as currently presented do not contain sufficient explicit supporting material. In the revised manuscript we will expand the abstract to more precisely summarize the theoretical and simulation contributions. We will also insert the key equations and derivations for the latency-energy trade-off directly into the Theoretical Analysis section and augment the Experiments section with full simulation parameters (quantization bit-widths, power levels, bandwidth values, device counts), baselines (FedAvg, vanilla split learning, quantized federated learning), metrics (accuracy, energy in Joules, latency in seconds, communication rounds), and error bars from multiple independent runs. These additions will allow independent verification of the claimed gains. revision: yes

  2. Referee: Theoretical Analysis section: No specific formulation of the latency-energy trade-off (e.g., expressions relating quantization bits, power, bandwidth, and convergence) is visible, making it impossible to assess whether the analysis is rigorous or merely descriptive.

    Authors: We agree that the current Theoretical Analysis section is insufficiently explicit. We will revise it to include the concrete mathematical formulations that relate quantization bit-width, transmit power, and bandwidth allocation to per-round latency and energy consumption, as well as the resulting impact on the convergence rate of the split federated fine-tuning procedure. The added derivations will make the rigor of the latency-energy trade-off analysis clear and directly address the referee's concern. revision: yes

Circularity Check

0 steps flagged

No significant circularity; no derivation chain or equations provided to inspect

full rationale

The supplied text consists solely of the abstract, which describes a proposed SFLAM framework, mentions a theoretical latency-energy analysis, and reports simulation-based superiority claims. No equations, parameter-fitting procedures, self-citations, ansatzes, or uniqueness theorems appear in the visible content. Without any load-bearing mathematical steps or citations that could reduce to self-definition or fitted inputs, the derivation chain cannot be walked and exhibits no circularity by the enumerated patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on abstract; no explicit free parameters, axioms, or invented entities are detailed in the provided text.

pith-pipeline@v0.9.0 · 5727 in / 990 out tokens · 27030 ms · 2026-05-22T21:03:23.037344+00:00 · methodology

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