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arxiv: 2411.11707 · v3 · submitted 2024-11-18 · 💻 cs.CL · cs.AI

Federated Co-tuning Framework for Large and Small Language Models

Tao Fan , Yan Kang , Guoqiang Ma , Lixin Fan , Shuoling Liu , Kai Chen , Qiang Yang This is my paper

Pith reviewed 2026-05-23 17:03 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords federated learninglarge language modelssmall language modelsco-tuningknowledge transferparameter-efficientNLP text generationprivacy-preserving
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The pith

A federated co-tuning framework lets server LLMs and client SLMs mutually improve performance through private adapter-based knowledge exchange.

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

The paper presents FedCoLLM as a method for co-tuning a central large language model with multiple client small language models. Lightweight adapters enable the server model to send knowledge to the clients while receiving domain-specific updates back, all without sharing raw client data. A sympathetic reader would care if this holds because it opens a path for distributed teams to upgrade their local models using powerful external resources and simultaneously strengthen the shared model, under typical privacy limits in NLP applications. The reported experiments across public models and text generation tasks indicate gains for the small models and near-equivalent results for the large model compared to direct fine-tuning.

Core claim

FedCoLLM is a parameter-efficient federated framework that uses lightweight adapters attached to SLMs to transfer server LLM knowledge to clients while enriching the LLM with client domain insights, achieving this exchange in a privacy-preserving way with low computational and communication overhead. Evaluations across various public LLMs and SLMs on NLP text generation tasks show that client SLMs improve notably with LLM assistance, while the co-tuned LLMs reach performance levels comparable to those from direct fine-tuning on client data.

What carries the argument

lightweight adapters attached to SLMs that enable bidirectional knowledge transfer in the federated co-tuning process

If this is right

  • Client SLMs achieve notable performance improvements on NLP text generation tasks when assisted by the server LLM.
  • The server LLM enhanced through FedCoLLM reaches performance comparable to direct fine-tuning on client data.
  • Knowledge exchange occurs while respecting data privacy and keeping computational and communication overhead low.
  • The framework works with various public LLMs and SLMs across multiple text generation tasks.

Where Pith is reading between the lines

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

  • If the adapters scale reliably, the same co-tuning pattern could apply to other distributed settings where large models serve many small-device clients.
  • Groups holding sensitive data might use this pattern to gain from external LLM capacity without exposing raw records.
  • Testing the method on non-text tasks or with much larger client counts would clarify whether the overhead savings persist.

Load-bearing premise

Lightweight adapters attached to SLMs can enable effective bidirectional knowledge transfer between server LLMs and client SLMs while preserving privacy and keeping computational and communication costs low.

What would settle it

An experiment in which SLMs trained under FedCoLLM show no performance gain over independent local training on the same tasks, or in which the co-tuned LLM underperforms a version directly fine-tuned on the pooled client data.

Figures

Figures reproduced from arXiv: 2411.11707 by Guoqiang Ma, Kai Chen, Lixin Fan, Qiang Yang, Shuoling Liu, Tao Fan, Yan Kang.

Figure 1
Figure 1. Figure 1: FedCoLLM (Federated parameter-efficient co-tuning of clients’ domain SLMs and the server’s LLMs. Clients’ SLMs learn from each other via federated fine-tuning of their adapter modules and transfer knowledge from and to the server’s LLM) 3.3 Computation and Communication Complexity One key advantage of FedCoLLM is its computational efficiency. By utilizing PEFT, it markedly decreases the parameters needing … view at source ↗
read the original abstract

By adapting Large Language Models (LLMs) to domain-specific tasks or enriching them with domain-specific knowledge, we can fully harness the capabilities of LLMs. Nonetheless, a gap persists in achieving simultaneous mutual enhancement between the server's LLM and the downstream clients' Small Language Models (SLMs). To address this, we propose FedCoLLM, a novel and parameter-efficient federated framework designed for co-tuning LLMs and SLMs. This approach is aimed at adaptively transferring server-side LLMs knowledge to clients' SLMs while simultaneously enriching the LLMs with domain insights from the clients. To accomplish this, FedCoLLM utilizes lightweight adapters in conjunction with SLMs, facilitating knowledge exchange between server and clients in a manner that respects data privacy while also minimizing computational and communication overhead. Our evaluation of FedCoLLM, utilizing various public LLMs and SLMs across a range of NLP text generation tasks, reveals that the performance of clients' SLMs experiences notable improvements with the assistance of the LLMs. Simultaneously, the LLMs enhanced via FedCoLLM achieves comparable performance to that obtained through direct fine-tuning on clients' data. Our code has been contributed to the FATE open-source project and is now publicly accessible at https://github.com/FederatedAI/FATE-LLM/tree/main/python/fate_llm/algo/fedcollm.

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 / 1 minor

Summary. The manuscript introduces FedCoLLM, a parameter-efficient federated co-tuning framework that uses lightweight adapters attached to SLMs to facilitate bidirectional knowledge transfer between a server-side LLM and client-side SLMs. The framework aims to improve SLM performance through LLM assistance while enriching the LLM with domain-specific knowledge from clients, all in a privacy-preserving manner with low computational and communication costs. Evaluations on public LLMs and SLMs for NLP text generation tasks are claimed to show notable SLM improvements and LLM performance comparable to direct fine-tuning. The implementation is open-sourced in the FATE project.

Significance. If the empirical claims hold, the work could contribute to the field by providing a practical method for co-adapting heterogeneous language models in federated settings. The open-sourcing of the code in FATE is a clear strength for reproducibility.

major comments (1)
  1. [Abstract] Abstract: the central claims of 'notable improvements' in SLM performance and LLM performance 'comparable' to direct fine-tuning are asserted without any quantitative results, baselines, error bars, ablation details, or specific metrics. This is load-bearing for the evaluation component of the central claim.
minor comments (1)
  1. The description of the adapter mechanism and knowledge exchange protocol would benefit from a high-level diagram or pseudocode to clarify the bidirectional transfer process.

Simulated Author's Rebuttal

1 responses · 0 unresolved

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

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claims of 'notable improvements' in SLM performance and LLM performance 'comparable' to direct fine-tuning are asserted without any quantitative results, baselines, error bars, ablation details, or specific metrics. This is load-bearing for the evaluation component of the central claim.

    Authors: We agree that the abstract would be strengthened by including concrete quantitative support for the central claims. In the revised manuscript we will update the abstract to report key metrics (e.g., average relative improvement on client SLMs across the evaluated tasks and the performance delta versus direct fine-tuning on the server LLM), while referencing the corresponding tables and figures. The full set of baselines, error bars, and ablation studies already appear in Sections 4–5; the revision will simply surface the most salient numbers in the abstract itself. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper proposes FedCoLLM, a parameter-efficient federated co-tuning framework that uses lightweight adapters for bidirectional knowledge transfer between server LLMs and client SLMs. Claims of SLM improvement and LLM performance comparable to direct fine-tuning rest on empirical evaluation across public models and NLP text generation tasks, with code released in FATE. No mathematical derivation chain, equations, fitted parameters renamed as predictions, or self-citations appear as load-bearing elements; the argument is self-contained via experimental results rather than any reduction of outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, no explicit free parameters, axioms, or invented entities are introduced; the approach builds on standard federated learning and adapter tuning concepts.

pith-pipeline@v0.9.0 · 5783 in / 1133 out tokens · 39483 ms · 2026-05-23T17:03:11.443752+00:00 · methodology

discussion (0)

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Forward citations

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