Core Problems Solved !

Core Problems Solved: View the SMP Repository on GitHub Security Vulnerabilities at Scale Traditional Federated Learning (FL) systems often fail when 33% of nodes are malicious. SMP is mathematically guaranteed to be resilient against Byzantine attacks even if up to 55.5% of nodes are compromised. Communication Bottlenecks: SMP optimizes efficiency by significantly reducing complexity. This reduces metadata overhead by 700,000x (e.g., shrinking data requirements from 40 TB down to 28 MB for 10 million nodes). Trust and Verification SMP eliminates the need to trust a central aggregator by using zk-SNARK proofs. Size: 200-byte proofs Speed: 10ms verification Benefit: Allows for massive updates without the need for re-execution. Data Sovereignty & Privacy It addresses "data rent" issues by ensuring raw data never leaves the edge device. It utilizes Differential Privacy (DP) with a verifiable "Privacy Budget" to prevent individual data leakage. Resource Constraints on Edge Devices Optimize