Federated-Learning on CuraSec

FISGuard: Defense Against Membership Inference in Federated LLMs

Mon, 31 Aug 2026 19:07:02 +0000

Engineer — Learn: Novel defense technique for federated fine-tuning pipelines; relevant if you run distributed LLM training with sensitive data, but no patch or configuration action needed today — research-stage only.
SOC/IR — Skip
Leader — Skip

Mon, 24 Aug 2026 13:10:29 +0000

Engineer — Learn: Academic research proposing a combined data-provenance watermarking and post-quantum secure aggregation scheme for federated learning; no exploitation signals or patch action required, but relevant if you are designing or hardening an FL pipeline.
SOC/IR — Skip
Leader — Skip

Mon, 20 Jul 2026 14:31:24 +0000

Engineer — Learn: Novel finding that 5G PDCCH scheduling metadata leaks enough temporal pattern to identify FL model architecture families, enabling targeted downstream attacks. No patch exists; worth factoring into FL-over-cellular deployment design (e.g., traffic shaping, scheduling obfuscation) before adopting this stack.
SOC/IR — Skip
Leader — Skip

Mon, 13 Jul 2026 14:30:14 +0000

Engineer — Learn: Academic architecture study combining homomorphic encryption and differential privacy for FL systems; no vulnerabilities or patches, but relevant for engineers designing privacy-preserving ML pipelines in healthcare or finance contexts.
SOC/IR — Skip
Leader — Learn: Research validates that FL with strong privacy controls can meet accuracy requirements in sensitive domains; useful background for evaluating AI/ML vendor privacy claims or shaping internal AI data-handling policy.