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arxiv: 2502.16691 · v2 · pith:TV3GXHJKnew · submitted 2025-02-23 · 💻 cs.CL · cs.DC· cs.MA

Responsible Federated LLMs via Safety Filtering and Constitutional AI

Eunchung Noh , Jeonghun Baek This is my paper

Pith reviewed 2026-05-23 02:16 UTC · model grok-4.3

classification 💻 cs.CL cs.DCcs.MA
keywords federated learninglarge language modelssafety filteringconstitutional AIresponsible AIAdvBench
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The pith

Integrating safety filtering and constitutional AI into federated LLM training improves safety by over 20% on AdvBench.

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

The paper examines how federated training of large language models can spread unsafe behavior when client data includes harmful content. It proposes embedding safety filtering to exclude bad examples and constitutional AI to steer outputs toward safer responses during the federated cycle. The goal is to produce a global model that remains less likely to generate inappropriate text while keeping the decentralized training structure intact. Readers would care because federated setups are used precisely when data privacy prevents central collection, yet the resulting models still need to avoid causing harm. The reported experiments measure a safety gain exceeding twenty percent on the AdvBench benchmark.

Core claim

The paper claims that safety filtering and constitutional AI can be added to the federated learning process for LLMs. This addition addresses the risk that harmful client data produces unsafe models which then get aggregated and redistributed. The integrated methods yield models that perform over twenty percent better on AdvBench safety evaluations compared with baseline federated training.

What carries the argument

Safety filtering to screen harmful content from client data combined with constitutional AI to enforce safety principles during response generation, both applied inside the federated aggregation steps.

If this is right

  • Aggregated global models distributed back to clients produce fewer unsafe responses.
  • Client data contributions no longer carry the same risk of contaminating the shared model.
  • Responsible AI techniques become usable inside decentralized LLM training pipelines.
  • Federated deployments in regulated domains gain a direct mechanism for reducing harmful outputs.

Where Pith is reading between the lines

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

  • The same integration pattern could apply to other generative models trained under federated constraints.
  • Additional tests on varying data sizes or added privacy noise would reveal whether the safety gains hold under stricter conditions.
  • Real deployments would still need separate checks that the safety improvements survive across languages and model sizes.

Load-bearing premise

Safety filtering and constitutional AI can be integrated into the federated learning process without degrading utility, convergence, or privacy guarantees.

What would settle it

An experiment that applies the same federated training setup with and without the two RAI methods and finds no safety gain or a loss on AdvBench while model accuracy on standard tasks stays flat or declines.

Figures

Figures reproduced from arXiv: 2502.16691 by Eunchung Noh, Jeonghun Baek.

Figure 1
Figure 1. Figure 1: For the responsible federated large language model [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of FedLLM. 𝑊 represents the model weights. The pretrained LLM (𝑊𝑃 ) remains frozen, and only the local LoRA weights (𝑊𝐿) are finetuned. these LLMs often contain billions of parameters (e.g., over 7 billion), training all parameters incurs high costs. Moreover, in FL, model parameters are frequently exchanged between clients and the server, significantly increasing communi￾cation costs for large mo… view at source ↗
Figure 3
Figure 3. Figure 3: For CAI, red and self-revised responses are collected over three conversation turns. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
read the original abstract

Recent research has increasingly focused on training large language models (LLMs) using federated learning, known as FedLLM. However, responsible AI (RAI), which aims to ensure safe and trustworthy responses, remains underexplored in this context. In FedLLM, client-side training data may contain harmful content, resulting in unsafe LLMs that can generate inappropriate responses. Aggregating such models into a global model and redistributing it to clients risks the widespread deployment of unsafe LLMs. To address this, we incorporate two well-established RAI techniques into FedLLM: safety filtering and constitutional AI. Our experiments show that these methods significantly improve LLM safety, achieving over 20% improvement on AdvBench.

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

3 major / 0 minor

Summary. The paper proposes incorporating safety filtering and constitutional AI into federated LLM training (FedLLM) to mitigate risks from harmful client-side data that could lead to unsafe global models, and reports that these techniques yield over 20% improvement on the AdvBench safety benchmark.

Significance. If the integration can be shown to preserve convergence, utility, and privacy while delivering the claimed safety gains, the work would address a genuine gap in responsible federated learning; however, the current manuscript supplies insufficient methodological detail to evaluate whether the result is attributable to the federated setting or to isolated non-federated safety passes.

major comments (3)
  1. [Abstract] Abstract: the central experimental claim of '>20% improvement on AdvBench' is presented without any description of the modified FedAvg-style update rule, the placement of the safety filter (client vs. server), or the constitutional critique step inside the federated loop; this information is load-bearing for attributing the gain to the proposed FedLLM integration rather than to a separate safety pass.
  2. [Abstract] Abstract / Methods: no baselines, statistical tests, communication-round counts, perplexity or downstream-task metrics, or privacy-budget accounting are supplied, preventing assessment of whether the safety techniques degrade utility or convergence in the federated regime.
  3. [Abstract] Abstract: the weakest assumption—that safety filtering and constitutional AI can be inserted without breaking federated convergence or privacy—is left implicit and untested in the reported results.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for these detailed comments on the abstract. We agree that additional methodological detail is required to substantiate the attribution of safety gains to the federated integration and to demonstrate that utility, convergence, and privacy are preserved. The revised manuscript will expand the abstract and methods sections accordingly while adding the requested metrics and analyses.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central experimental claim of '>20% improvement on AdvBench' is presented without any description of the modified FedAvg-style update rule, the placement of the safety filter (client vs. server), or the constitutional critique step inside the federated loop; this information is load-bearing for attributing the gain to the proposed FedLLM integration rather than to a separate safety pass.

    Authors: We agree that the abstract omits these load-bearing details. In the revision we will add a concise description of the modified FedAvg update (safety filter applied client-side prior to local updates, followed by server-side aggregation), the client-side placement of the safety filter, and the insertion of the constitutional critique step after each local training round before aggregation. This will make explicit how the >20% AdvBench gain arises from the integrated FedLLM procedure rather than an isolated safety pass. revision: yes

  2. Referee: [Abstract] Abstract / Methods: no baselines, statistical tests, communication-round counts, perplexity or downstream-task metrics, or privacy-budget accounting are supplied, preventing assessment of whether the safety techniques degrade utility or convergence in the federated regime.

    Authors: We acknowledge the omission. The revised version will include (i) standard FedAvg and non-federated safety baselines, (ii) statistical significance tests on the AdvBench results, (iii) communication-round counts and convergence curves, (iv) perplexity and downstream-task performance (e.g., MMLU, GSM8K), and (v) privacy-budget accounting under the federated DP setting. These additions will allow direct evaluation of any utility or convergence trade-offs. revision: yes

  3. Referee: [Abstract] Abstract: the weakest assumption—that safety filtering and constitutional AI can be inserted without breaking federated convergence or privacy—is left implicit and untested in the reported results.

    Authors: We agree the assumption was left implicit. The revision will add explicit experiments and analysis (convergence plots with and without the safety modules, privacy leakage metrics, and communication cost) demonstrating that the inserted components do not break convergence or violate the privacy guarantees of the federated protocol. If the current experiments are insufficient, we will run the additional ablations. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical safety gains reported without derivation or self-referential fitting

full rationale

The paper's central claim is an experimental outcome (>20% AdvBench improvement) obtained by incorporating established RAI techniques into FedLLM. No equations, parameter-fitting steps, or derivation chain are presented that could reduce to the inputs by construction. The result is framed as measured performance after integration rather than a prediction derived from the same data or a self-citation that bears the load of the claim. Self-citations, if present, are not required to justify the reported numbers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no information on free parameters, axioms, or invented entities.

pith-pipeline@v0.9.0 · 5645 in / 890 out tokens · 39385 ms · 2026-05-23T02:16:44.979061+00:00 · methodology

discussion (0)

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Reference graph

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