@inproceedings{wang-etal-2026-prism,
title = "{PRISM}: Private Rank-Invariant Subspace Memory for Federated Continual Fine-Tuning under Local Differential Privacy",
author = "Wang, Ziyi and
Liu, Liang and
Meng, Zhu and
Zhao, Zhicheng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.313/",
pages = "7204--7223",
ISBN = "979-8-89176-459-0",
abstract = "Privacy-preserving adaptation of pretrained language models requires continual fine-tuning as private client distributions shift, while protecting individual samples under local differential privacy (LDP). In streaming differentially private federated learning, full-rank Low-Rank Adaptation (LoRA) adapters expose many trainable directions to DP noise, and later domains may overwrite directions that remain relevant to earlier domains. We propose Private Rank-Invariant Subspace Memory (PRISM), a fixed-rank LoRA framework that schedules the active plastic rank over the stream and applies shrinkage-gated memory writes to DP-protected aggregate updates. Server-side consolidation stores retained high-SNR directions in an orthogonal memory and remains pure post-processing of LDP updates. On MNLI genre and Sentiment3, PRISM stabilizes adaptation under strict privacy budgets while preserving a strong public initialization. At $\epsilon=2$ , it reaches 79.8{\%} and 92.3{\%} final average accuracy, compared with 52.3{\%} and 85.0{\%} for full-rank DP-FedSVD. Ablations indicate that rank scheduling accounts for most strict-budget recovery, and shrinkage-gated memory contributes a smaller retention gain and modestly improves over the strongest static low-rank control on MNLI."
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<abstract>Privacy-preserving adaptation of pretrained language models requires continual fine-tuning as private client distributions shift, while protecting individual samples under local differential privacy (LDP). In streaming differentially private federated learning, full-rank Low-Rank Adaptation (LoRA) adapters expose many trainable directions to DP noise, and later domains may overwrite directions that remain relevant to earlier domains. We propose Private Rank-Invariant Subspace Memory (PRISM), a fixed-rank LoRA framework that schedules the active plastic rank over the stream and applies shrinkage-gated memory writes to DP-protected aggregate updates. Server-side consolidation stores retained high-SNR directions in an orthogonal memory and remains pure post-processing of LDP updates. On MNLI genre and Sentiment3, PRISM stabilizes adaptation under strict privacy budgets while preserving a strong public initialization. At ε=2 , it reaches 79.8% and 92.3% final average accuracy, compared with 52.3% and 85.0% for full-rank DP-FedSVD. Ablations indicate that rank scheduling accounts for most strict-budget recovery, and shrinkage-gated memory contributes a smaller retention gain and modestly improves over the strongest static low-rank control on MNLI.</abstract>
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%0 Conference Proceedings
%T PRISM: Private Rank-Invariant Subspace Memory for Federated Continual Fine-Tuning under Local Differential Privacy
%A Wang, Ziyi
%A Liu, Liang
%A Meng, Zhu
%A Zhao, Zhicheng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-prism
%X Privacy-preserving adaptation of pretrained language models requires continual fine-tuning as private client distributions shift, while protecting individual samples under local differential privacy (LDP). In streaming differentially private federated learning, full-rank Low-Rank Adaptation (LoRA) adapters expose many trainable directions to DP noise, and later domains may overwrite directions that remain relevant to earlier domains. We propose Private Rank-Invariant Subspace Memory (PRISM), a fixed-rank LoRA framework that schedules the active plastic rank over the stream and applies shrinkage-gated memory writes to DP-protected aggregate updates. Server-side consolidation stores retained high-SNR directions in an orthogonal memory and remains pure post-processing of LDP updates. On MNLI genre and Sentiment3, PRISM stabilizes adaptation under strict privacy budgets while preserving a strong public initialization. At ε=2 , it reaches 79.8% and 92.3% final average accuracy, compared with 52.3% and 85.0% for full-rank DP-FedSVD. Ablations indicate that rank scheduling accounts for most strict-budget recovery, and shrinkage-gated memory contributes a smaller retention gain and modestly improves over the strongest static low-rank control on MNLI.
%U https://aclanthology.org/2026.emnlp-main.313/
%P 7204-7223
Markdown (Informal)
[PRISM: Private Rank-Invariant Subspace Memory for Federated Continual Fine-Tuning under Local Differential Privacy](https://aclanthology.org/2026.emnlp-main.313/) (Wang et al., EMNLP 2026)
ACL