CVFL-Pro: A Collusion-Resistant Verification Federated Learning Framework With Adaptive Communication Optimization
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CVFL-Pro is a new federated learning framework (a system where AI models are trained across multiple computers without sharing raw data) that prevents malicious servers from cheating during model training while reducing communication costs. The framework uses cryptographic techniques like Shamir's secret sharing (a method to split secrets so no single party can reconstruct them alone) and an adaptive compression algorithm that automatically adjusts how much data is sent based on gradient changes, achieving up to 95.81% reduction in communication overhead compared to existing methods.
The source describes the CVFL-Pro framework itself as the solution. Key technical components include: using 'a mask and Shamir's secret sharing for privacy protection,' combining 'a lightweight MAC scheme and auxiliary nodes to achieve efficient verifiability,' and designing 'an adaptive communication optimization algorithm (AOTop-k) which dynamically adjusts the compression rate based on the gradient magnitude and the gradient variation between rounds.' The paper demonstrates that this framework 'reduces communication overhead by 95.81% compared to SecAgg' while maintaining accuracy.
Original source: http://ieeexplore.ieee.org/document/11612926
First tracked: July 27, 2026 at 08:04 PM
Classified by LLM (prompt v3) · confidence: 85%