federated-learning-security
CommunityAudit federated learning for poisoned updates.
Software Engineering#privacy-preserving#server-hardening#federated-learning#secure-aggregation#byzantine-robustness#gradient-poisoning
Authormaruakshay
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Federated learning security reviews mitigate poisoned gradient updates, model tampering by malicious participants, aggregation server compromises, Byzantine fault tolerance gaps, and privacy leakage via gradient inversion.
Core Features & Use Cases
- Validate and harden aggregation against Byzantine faults using robust aggregators (e.g., coordinate-wise median, Krum) and prevent naive FedAvg domination.
- Enforce privacy protections with secure aggregation, local differential privacy, and hardened server controls (mTLS, audit logs, signed checkpoints).
- Apply per-participant anomaly detection and monitoring across rounds to detect consistently dissimilar updates.
Quick Start
Review a federated learning deployment and implement robust aggregation, privacy protections, and server hardening in your ML workflow.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: federated-learning-security Download link: https://github.com/maruakshay/mii-ai-security/archive/main.zip#federated-learning-security Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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