FVCC: Enabling Fast and Verifiable Coded Computation for Robust Distributed Learning

Distributed Learning (DL, training AI models across multiple computers) faces problems when some computers are slow (stragglers) or malicious (Byzantine nodes, computers that send incorrect data). FVCC is a new framework that uses coded computing (a technique that adds redundancy so missing data can be recovered) with faster decoding and verification methods to make distributed learning more robust and efficient. The system uses a bidirectional two-dimensional ZigZag Decoding algorithm to recover data quickly and Freivalds' algorithm (a lightweight verification method) to detect dishonest computers.