INTEL_REPORT
arXiv — Cryptography & Security (cs.CR) · published 6/9/2026, 4:00:00 AM · TLP amber
Summary
Ingested excerpt (first ~500 chars of normalized text).
Quantifying and Defending against the Privacy Risk in Logit-based Federated Learning arXiv:2606.08252v1 Announce Type: new Abstract: Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among clients. Unlike traditional parameter-based FL methods that exchange model weights or gradients during training, emerging logit-based FL approaches share model outputs (logits) on public data. This strategy promotes model heter…
https://arxiv.org/abs/2606.08252
sha256:5e78aeb08ea80f584d1fe80a0a1dfe79e093c59652ab648ddaeee95c99a789d3
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ATT&CK techniques
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