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Exploring CKKS Parameter Trade-offs for Privacy-Preserving Personalized Federated Learning

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arXiv:2606. 08521v1 Announce Type: new Abstract: Privacy-preserving Personalized Federated Learning (PFL) enables clients to collaboratively train personalized models without exposing raw data, but exchanged model updates remain vulnerable to inference attacks from honest-but-curious servers.

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Authors: Kamolchanok Saengtong, Phanwadee Sinthong, Norrathep Rattanavipanon
Released: 2026-06-09 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Exploring CKKS Parameter Trade-offs for Privacy-Preserving Personalized Federated Learning overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, persona, rect, under, eval, line, rate, implement. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2606.08521

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