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FAVoR: Measuring and Mitigating Author-Style Homogenization in Federated Personalized Generation

arXiv 2609.30968 ↗First posted 25 Sep 2026cs.CLcited by 0

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Abstract (arXiv)

Large language models are increasingly used as personalized writing assistants, but adapting a model across many authors can compromise individual writing style by pulling author-specific signals toward a shared register. Federated parameter-efficient fine-tuning (PEFT) offers a data-local setting for this multi-author adaptation problem: clients keep author text local while sharing compact adapter updates. However, we show that standard aggregation can preserve continuation utility while making different authors' generations less distinguishable in style space, a failure mode we define as author-style homogenization. We evaluate author-style retention with Angular Style Classification Encoder (ASCE)-based diagnostics on our main BlogText benchmark and ASCE-independent external authorship verification. Using this protocol, we find that common federated PEFT baselines can preserve semantic utility while averaging out author-specific signals. To address this homogenization, we instantiate FAVoR (Federated Authorial Voice Retention), an author-style residual mechanism for federated PEFT. FAVoR uses a shared-private adapter design: clients upload shared-adapter updates while retaining author-specific residual corrections locally. Across BlogText and external Mythos-Reddit validation, FAVoR improves author-style retention over standard and personalized federated PEFT baselines. These gains come with small continuation-utility trade-offs and are supported by component ablations, external verification, and cold-start transfer.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 29 references that are in the map. First posted on arXiv 25 Sep 2026.

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19 of 29 references.

References · 19

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  12. 2024FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptationscited by 146
7 more
  1. 2024Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resourcescited by 93
  2. 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 85
  3. 2024AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuancescited by 60
  4. 2024FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuningcited by 59
  5. 2024Dual-Personalizing Adapter for Federated Foundation Modelscited by 55
  6. 2024StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examplescited by 35
  7. 2025Whose story is it? Personalizing story generation by inferring author stylescited by 6

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