PaperlayerView exact revision on arXiv

Source-linked AI summary

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

Lu Han, Jingyao Zhang, Katy Ilonka Gero, Nguyen H. Tran

arXiv:2609.30968v1cs.CL

TL;DR

Federated adaptation for personalized writing can preserve continuation quality while making authors’ styles less distinguishable. FAVoR combines shared adapters with local author-style residuals and evaluates retention with ASCE-based diagnostics and external verification. Across BlogText and Mythos-Reddit, it improves author-style retention over federated PEFT baselines with small, mixed continuation-utility changes.

  • Problem

    Federated author-level adaptation must support held-out continuation without losing the stylistic distinctions that personalization is meant to preserve.

    Source: §1 · p. 1

  • Method

    FAVoR combines aggregated shared-adapter updates with local author-specific residuals and evaluates style retention using ASCE diagnostics and external authorship verification.

    Source: §1 · p. 2, §3.2 · p. 3

  • Results

    Across BlogText and Mythos-Reddit, FAVoR shows the strongest collaborative author-style retention over standard and personalized federated PEFT baselines, with small, mixed continuation-utility changes.

    Source: §4.2 · p. 7

  • Takeaways & Limitations

    The results identify the residual aggregation boundary, rather than style-alignment loss alone, as the main contributor to FAVoR’s author-style gains.

    Source: §4.2 · p. 7, §5 · p. 9

    The operational style metrics do not exhaust social, pragmatic, or reader-perceived notions of authorial voice, and human authenticity judgments are outside the study’s scope.

    Source: Limitations · p. 9

Abstract

from arXiv · show

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.

1 Introduction

Federated adaptation can improve continuation while eroding the stylistic distinctions personalization is meant to preserve. The paper defines this failure mode as author-style homogenization and introduces an evaluation protocol and FAVoR to address it.

1 Introduction

  • Author-level non-IID data can make federated averaging mix unrelated or opposing author-specific correction directions, shifting generations toward a shared register.Keeping raw text local alone does not decide which adaptation directions should be shared and which should remain client-specific.Source: §1 · p. 1
  • The evaluation tests held-out prompts for each author and separates author signal from topical content using an author-disjoint ASCE-based signature space and external verification.Figure 1 reports that the learned space reduces the topic–author origin-fit slope from 0.72 to 0.47 relative to raw computational-linguistic features.Source: §1 · p. 1
  • FAVoR separates transferable continuation updates in a shared adapter from author-specific residual corrections kept local and outside aggregation.The paper frames author-style homogenization as loss of cross-author stylistic distinguishability despite preserved fluency and semantic plausibility.Source: §1 · pp. 1–2
  • Across BlogText and Mythos-Reddit, FAVoR improves author-style retention over standard and personalized federated PEFT baselines with a favorable retention–utility trade-off.The authors report small, mixed changes in continuation utility.Source: §1 · p. 2

2 Related Work

Prior work supplies federated personalization, PEFT, authorship-conditioned generation, and style representations, but the paper distinguishes its focus on federated held-out continuation and author-style retention.

Federated Personalization and PEFT for LLMs

  • Personalized federated learning addresses client heterogeneity through client-specific objectives, proximal regularization, or shared/client-specific adaptation structures (McMahan et al., 2017; Lin et al., 2022; T. Dinh et al., 2020; Li et al., 2021, 2020; Qi et al., 2024; Yang et al., 2024).Federated learning combined with parameter-efficient adaptation commonly uses low-rank adapters (LoRA) to reduce communication and memory cost.Source: §2 · p. 2
  • FAVoR uses a global–local adapter pattern as a communication design, while defining the local component specifically as an author-style residual kept outside aggregation.Related federated adapter work includes LoRA-based approaches to reduce communication and memory cost.Source: §2 · p. 2

Authorship-Conditioned Generation and Evaluation

  • Held-out continuation differs from formality transfer because it requires open-ended generation that preserves coherence, distributional fit, and author-specific style.The paper adds author-style retention, inter-author separability, and collapse measures to utility metrics such as MAUVE and BERTScore (Pillutla et al., 2023; Zhang* et al., 2020).Source: §2 · p. 2

Style and Authorship Representation Learning

  • Authorship representations can support verification and cross-domain transfer while also carrying domain entanglement, and the paper uses author/style representations for evaluation and local alignment targets.StyleDistance addresses topic leakage with content-controlled comparisons, while the paper connects its normalized space to ArcFace-style metric learning.Source: §2 · p. 2

3 Method

FAVoR studies author-non-IID federated held-out continuation with a shared–private adapter boundary: shared continuation updates are aggregated, while author-specific residuals and style controls remain local. A fixed ASCE supplies local style targets, and clients perform distinct shared and private updates before the server aggregates shared deltas.

3.1 Mechanism Constraint: Shared Competence and Private Style

  • FAVoR stores each private adapter as a residual pack relative to that client’s shared endpoint, reconstructing the personalized model from the shared adapter and local residual without assuming effective-weight additivity.Only the shared adapter is uploaded, so residual packs are not averaged across authors.Source: §3.1 · p. 3

3.2 Angular Style Classification Encoder

  • ASCE is a fixed encoder trained with an angular-margin authorship objective to make same-author texts compact and different-author texts separable, and clients use it locally for style measurement.The classifier head is discarded after training; the encoder is frozen during federated learning, and each client constructs a local author target from its reference set.Source: §3.2 · pp. 3–4

3.3 Local Shared and Private Updates

  • Each sampled client first trains a shared copy with task loss and proximal stabilization, then updates the private residual using task loss plus cosine-distance style alignment to its local author target.The style signal uses teacher-forced hidden states pooled over response tokens and a lightweight projection head; the projection head is discarded after the private stage.Source: §3.3 · pp. 4–5
  • The style-alignment gradient is applied only to private residual parameters, isolating author-specific correction from the public aggregation space.The alignment weight is warmed up over the first 5% of private-stage steps in each round and is set to 0.3 in the experiments.Source: §3.3 · p. 5

3.4 Federated Aggregation

  • The server broadcasts and aggregates shared-adapter deltas, while training corpora, author-reference sets, hidden states, style-control signals, and residuals remain on device.This is a data-locality claim, not a formal privacy guarantee: shared deltas may still encode memorized text or identifying information, and the method does not implement secure aggregation or differential privacy.Source: §3.4 · p. 5

4 Experiment

The experiments evaluate author-style retention alongside continuation utility, using BlogText and external verification. Across datasets, FAVoR leads collaborative baselines in author-style retention, with small, mixed utility changes.

4 Experiment

  • The main BlogText evaluation uses a 50-author federated split, with each client representing one author and raw text kept local.The evaluation also includes Mythos-Reddit external validation and a small human 2AFC audit used as a perceptual sanity check, not a full preference or authenticity evaluation.Source: §4 · p. 6

4.1 Signature-Space Validity

  • The source-space diagnostic compares compact representation measures against RoBERTa, StyleDistance, LUAR, and STAR before using ASCE to measure author-style retention.Coordinate-level author/topic discriminability is reported separately in Table 8.Source: §4.1 · p. 6, Table 1 · p. 6

4.2 Main Results and Ablations

  • A method-blinded judge found no significant generic-quality difference, while preferring FAVoR in 61.2% of non-ties in the author-voice test.The human audit matched FAVoR continuations to the intended author more often than Ditto (91.7% vs. 68.3% vote-level accuracy); it is supporting perceptual evidence only.Source: §4.2 · p. 7
  • The ablations indicate that shared–private residual aggregation accounts for most author-style retention, while alignment adds a smaller, metric-dependent change.Adding ASCE alignment to Ditto recovers only a fraction of FAVoR’s author-accuracy gain, whereas FAVoR without alignment already attains most of it.Source: §4.2 · p. 7, Table 4 · p. 8
  • Figure 3 shows FAVoR’s personalized adapters concentrate LoRA update energy in MLP projections rather than distributing it uniformly across target modules.The pattern is consistent with private residuals storing structured corrections on top of the shared endpoint.Source: §4.2 · p. 7, Figure 3 · p. 8
  • Across both datasets, FAVoR shows the strongest collaborative author-style retention, with small, mixed changes in continuation utility.The main comparison covers collaborative federated methods; Table 3 provides external-verifier results on BlogText.Source: §4.2 · p. 7, Table 2 · p. 8, Table 3 · p. 8

4.3 Cold-Start Transfer to Unseen Authors

  • At K = 8, FAVoR improves over local-only adaptation in author accuracy, macro-F1, and target similarity, with the clearest shared-initialization advantage at that low-resource setting.With fixed 32-text evaluation prototypes, FAVoR exceeds local-only accuracy by 0.119 (95% CI [0.050, 0.203]); neither method is reliably superior at K = 16–24.Source: §4.3 · pp. 8–9, Table 5 · p. 8

5 Conclusion

The conclusion frames author-style homogenization as a utility-preserving failure in federated personalized continuation. FAVoR mitigates it mainly by keeping author-specific residuals outside aggregation, while broader author populations, writing domains, and stronger privacy mechanisms remain future-work directions.

5 Conclusion

  • The paper treats author-style homogenization as a failure that can occur even when standard utility metrics remain acceptable.Its evaluation protocol is intended to reveal this beyond metrics such as BERTScore and MAUVE.Source: §5 · p. 9
  • FAVoR separates aggregated shared updates from local author-style residuals, and the residual aggregation boundary accounts for more retention than alignment alone.The conclusion characterizes the method as an author-style residual mechanism on a shared-private PEFT design.Source: §5 · p. 9
  • The analysis calls for extension to larger author populations, broader writing domains, and stronger privacy mechanisms for author-specific adaptation.Source: §5 · p. 9

Limitations

The evaluation measures author style operationally, but does not cover social, pragmatic, or reader-perceived voice. Its learned style spaces may still encode corpus-specific regularities, and the privacy and experimental claims have defined boundaries.

Evaluation scope

  • Human judgments of perceived authenticity are outside scope, and learned style spaces may encode topic, genre, demographic artifacts, or other corpus-specific regularities.The BlogText ASCE is trained centrally on authors disjoint from the FL roster; external verifiers are paired with ASCE metrics.Source: Limitations · p. 9

Privacy boundary

  • The privacy claim covers data locality during FL, not differential privacy, secure aggregation, or resistance to gradient- or adapter-inversion attacks.Private residual packs are not uploaded or released.Source: Limitations · p. 9

Experimental scope

  • Experiments cover English public writing domains and one backbone family; the single-seed 7B probe changes both scale and instruction-tuning status.The authors characterize the 7B run as robustness evidence, not a clean scaling ablation.Source: Limitations · p. 9

Ethical Considerations

The paper uses public writing corpora while recognizing that public availability is not blanket consent for author-style modeling. It limits data release and recommends safeguards against deceptive imitation, while its small human audit is only a perceptual attribution check.

Consent, licensing, and data use

  • Public availability is not blanket consent for author-style modeling, and the corpora may contain identifiable details or offensive content.The authors did not conduct a comprehensive PII/offensive-content audit or fully de-identify the source corpora.Source: Ethical Considerations · p. 9
  • For reporting and release, the authors use aggregate metrics, anonymize handles and signatures in examples, and do not redistribute raw or author-linkable materials.Released code users must obtain datasets from their original sources and follow applicable licenses, platform terms, and deletion requests.Source: Ethical Considerations · p. 9

Human annotation

  • The three-annotator 2AFC audit was a small perceptual attribution check, not a preference, authenticity, or quality evaluation.Annotators gave informed consent, were compensated, and received anonymized excerpts; only aggregate results are reported.Source: Ethical Considerations · pp. 9–10

Model, evaluator, and release policy

  • Because author-style adaptation can support deceptive imitation, the paper recommends author consent, user disclosure, access controls, and safeguards against unauthorized identity use.The experiments use public evaluator artifacts and do not release raw or author-linkable generated texts, private residual packs, or author-specific checkpoints.Source: Ethical Considerations · p. 10

A FAVoR Training Details

The appendix details FAVoR’s shared-private training and evaluation procedures, then tests retention across diagnostic, scale, data-volume, imbalance, and cold-start settings. The reported advantages persist in several tested comparisons, with stated scope and robustness caveats.

A.1 Complete Training Pseudocode

  • Algorithm 2 makes explicit the client-local and uploaded quantities: clients update private residuals locally, while shared-adapter updates are sent for server aggregation.The procedure samples clients, broadcasts the shared adapter, and retains client states alongside the final shared adapter.Source: §A.1 · p. 12

B.1 Experimental Protocol and Metric Definitions

  • The evaluation distinguishes source and generated-text authorship measures, topic-controlled verification, prototype classification, and paired-reference continuation metrics.Higher AUC and lower EER are better; BERTScore and SemSim compare matched continuations, while MAUVE compares aggregate distributions.Source: §B.1 · p. 12

B.3 Adapter-Space Diagnostic

  • Figure 3 shows update energy concentrated in MLP projections, especially gate_proj and up_proj, with comparatively little in k_proj and v_proj.The diagnostic summarizes normalized energy across seven target modules and is supportive rather than causal mechanism evidence; component ablations in Table 4 provide the main mechanism evidence.Source: §B.3 · p. 13

B.6 7B Base-Model Sensitivity

  • On the single-seed 7B-base sensitivity run, FAVoR is strongest among collaborative methods on each author-style retention column and beats FedAvg by 0.310 author accuracy and 0.183 verification AUC.All generated continuations were retained after filtering; the run changes both model scale and instruction-tuning status.Source: §B.6 · p. 13

B.7 100-Author Scale Stress Test

  • At 100 authors, FAVoR recovers roughly 70% of FedAvg-to-local-only separability accuracy gap, 74% of macro-F1, 81% of verification AUC, and 72% of silhouette.FedAvg shows low author separability, negative silhouette, and high verification error; FAVoR is close to Ditto on verification AUC/EER, while Ditto has higher MAUVE.Source: §B.7 · pp. 13–14

B.8 Text-Volume and Author-Imbalance Sensitivity

  • Across four-fold text budgets, FAVoR retains higher Gen–Gen AUC and lower EER, while BERTScore differences remain small.The comparisons use balanced nested subsets and fixed per-client compute at 25%, 50%, and 100% of a 904,378-unit reference budget.Source: §B.8 · p. 14
  • Across six matched seed-by-condition comparisons, FAVoR has higher AUC and lower EER; its mean AUC ranges from 0.753 to 0.738 as allocation changes.Absolute mean BERTScore differences are at most 0.0018; the evidence supports robustness within the tested range, not invariance to imbalance.Source: §B.8 · p. 14

B.9 Cold-Start New-Author Details

  • In the cold-start K-sweep, FAVoR’s clearest advantage is at K = 8, differences diminish by K = 16, and no reliable winner appears at K = 16–24.Both K-shot and fixed-prototype diagnostics support a diminishing shared-initialization advantage, not a universal crossover threshold.Source: §B.9 · p. 15, Table 14 · p. 15

B.10 Human 2AFC Audit

The human audit found that FAVoR continuations were more often attributed to the intended author than Ditto continuations. Reference-free judging favored FAVoR on voice match, while showing no significant generic-quality difference after correction.

B.10 Human 2AFC Audit

  • The audit tested author attribution rather than writing quality, using 50 items, including 20 Ditto and 20 FAVoR continuations, with three annotators per item.Its small size and the possibility that profile choices reflect topical, biographical, or content cues constrain interpretation.Source: §B.10 · pp. 15–16
  • FAVoR continuations were attributed to the intended author more often than Ditto continuations: 91.7% versus 68.3% vote-level accuracy and 95.0% versus 70.0% majority accuracy.The audit treats this as complementary evidence that retained author signal is human-perceivable, not as a standalone preference or authenticity evaluation.Source: §B.10 · p. 16

B.11 Reference-Free Quality and Voice-Match Judges

  • The judge preferred FAVoR to Ditto in 61.2% of non-ties, while no generic-quality difference was significant after correction.The voice-match result supports FAVoR when author-style retention is the primary objective; the quality analysis does not establish superiority or equivalence.Source: §B.11 · p. 17

B.12 External Verifier Robustness

  • External verification compares same-author generated/source pairs with different-author pairs drawn from the same usable metadata-topic bucket, using embedding and stylometric verifiers.The reported compact averages and evaluator breakdown are in Tables 3 and 17.Source: §B.12 · p. 17

B.13 Author-Disjoint ASCE Training and Evaluation Details

The author-disjoint ASCE encoder supports both local style alignment and BlogText style evaluation. Across its source-anchored and generated-text views, FAVoR leads collaborative methods on key author-retention measures.

B.13 Author-Disjoint ASCE Training and Evaluation Details

  • The production BlogText ASCE encoder was trained on 50 authors disjoint from the FL roster, then frozen with its classifier head and prototypes discarded.Its training used 4,959 texts and its validation used 742; it does not observe FL training texts, held-out prompts, generated continuations, or main-roster author identities.Source: §B.13 · p. 18
  • Table 21 compares generated continuations with source-train prototypes and gen-to-source pairs, while Table 22 expands generated-text author-style results with pooled and diagnostic references.Both views use the author-disjoint ASCE space.Source: §B.13 · p. 19
  • FAVoR is the strongest collaborative method in both ASCE views, ranking best on expanded-view author recovery and verification EER and close to local-only on F1 and AUC.In the gen-to-source view it ranks second overall on prototype recovery and AUC and best overall on EER; the no-ASCE variant is also competitive.Source: §B.13 · p. 19

B.14 Independent ASCE-B Re-evaluation

FAVoR’s author-style retention remains strong under an independently trained ASCE evaluator, at different evaluator scales, and in Mythos-Reddit validation. A bounded FedDPA probe found that changing its gate left author accuracy unchanged.

B.14 Independent ASCE-B Re-evaluation

  • FAVoR remained highest under independently trained ASCE-B, and FAVoR without ASCE alignment exceeded all non-FAVoR collaborative baselines.The result indicates that same-checkpoint circularity cannot solely explain the gain, though ASCE-B shares ASCE-A’s architecture, objective, and source domain.Source: §B.14 · p. 20

B.15 ASCE Evaluator-Scale and Cross-Domain Transfer

  • Across tested ASCE scales, BlogText retained the ordering FAVoR > Ditto > FedAvg, while Mythos results showed nontrivial representation transfer within the tested settings.The evidence does not establish a minimum ASCE author count, prototype-free zero-shot attribution, or universal out-of-domain robustness.Source: §B.15 · p. 20

B.16 External Domain Validation

  • On Mythos-Reddit, FAVoR was again the strongest collaborative federated method on the style-separability columns.The validation used 50 r/WritingPrompts authors and 300 balanced held-out continuations per run.Source: §B.16 · p. 20

B.17 FedDPA Gate Sensitivity Probe

  • In the bounded FedDPA gate probe, dynamic gating left author accuracy unchanged and BERTScore nearly identical, while slightly lowering verification AUC and SemSim.The probe used 100 matched BlogText held-out prompts; the reported FedDPA rows use the audited summary gate.Source: §B.17 · pp. 20–21

B.18 Component-Ablation Significance and Run Checks

Paired comparisons support FAVoR’s author-style improvements over several baselines, while semantic-utility differences are smaller and not uniformly favorable. The appendices detail component-ablation results, run settings, and communication costs.

B.18 Component-Ablation Significance and Run Checks

  • External ablations show small and mixed alignment gains over the no-ASCE variant, while both private-residual variants are consistently stronger than the Global ASCE control.Paired tests use matched seed-author-prompt examples; the continuation quality filter removes outputs under 20 whitespace-delimited tokens or with distinct-bigram ratio below 0.15.Source: §B.18 · pp. 21–22

B.19 Hyperparameters, Efficiency, and Communication Cost

  • 72 client updates per seed produce 8.03 GiB of client-to-server adapter payloads, or 16.07 GiB counting broadcasts in both directions.Private residual packs are not communicated; retaining one final residual per 50-author evaluation roster would require 5.58 GiB if stored as dense fp32 LoRA factor-delta tensors.Source: §B.19 · p. 22

B.20 Statistical Testing

  • Paired evidence supports FAVoR’s author-style improvements over Ditto, pooled PEFT, FedProx, and FedDPA, while semantic-utility differences are smaller and not uniformly in FAVoR’s favor.Comparisons use method-pair intersections of quality-passed examples, so their means may differ from full-coverage summary-table means.Source: §B.20 · p. 23
Loading 2609.30968v1…