发表机构
The University of Sydney（悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对联邦个性化生成中作者风格同质化问题，提出FAVoR机制，通过共享-私有适配器残差设计，在保持续写效用的同时显著提升作者风格保留，并经外部验证和消融实验支持。
AI 中文摘要
大型语言模型越来越多地被用作个性化写作助手，但跨多个作者适配模型可能会通过将作者特定信号拉向共享语域而损害个人写作风格。联邦参数高效微调（PEFT）为这种多作者适配问题提供了一种数据本地化设置：客户端在本地保留作者文本，同时共享紧凑的适配器更新。然而，我们表明，标准聚合可以在保持续写效用的同时，使不同作者的生成在风格空间上变得不那么可区分，我们将这种失败模式定义为作者风格同质化。我们使用基于角度风格分类编码器（ASCE）的诊断方法在我们的主要BlogText基准上评估作者风格保留，并使用独立于ASCE的外部作者身份验证。利用这一协议，我们发现常见的联邦PEFT基线可以在保留语义效用的同时平均掉作者特定信号。为了解决这种同质化问题，我们实例化了FAVoR（联邦作者声音保留），一种用于联邦PEFT的作者风格残差机制。FAVoR采用共享-私有适配器设计：客户端上传共享适配器更新，同时在本地保留作者特定的残差修正。在BlogText和外部Mythos-Reddit验证中，FAVoR相对于标准和个性化联邦PEFT基线提高了作者风格保留。这些收益伴随着较小的续写效用权衡，并得到组件消融、外部验证和冷启动迁移的支持。
英文摘要
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.
