Distributed or federated training where some workers send bad gradients, by mistake or on purpose. This is similar to resilient consensus: the server, or each peer, has to aggregate vectors it can’t fully trust.
Aggregators
- Krum (Blanchard, El Mhamdi, Guerraoui and Stainer, 2017) picks the gradient closest to its nearest neighbours.
- Coordinate-wise median and trimmed mean (Yin, Chen, Ramchandran and Bartlett, 2018), with statistical error rates. The trimmed mean is a close cousin of W-MSR.
In multi-agent RL
FRAC-MARL (2026) is a decentralised actor-critic method that tolerates corrupted messages on links. Each agent uses redundancy in two-hop messages to pick out the reliable ones, and under linear parameterisations the parameters converge to the attack-free limit, where earlier methods only got close.
Relevance
Robot teams that learn together (shared maps, shared policies) have the same problem, with less compute, less reliable links, and physical consequences for mistakes.
References
- P. Blanchard, E. M. El Mhamdi, R. Guerraoui, J. Stainer. Machine learning with adversaries: Byzantine tolerant gradient descent. NeurIPS, 2017.
- D. Yin, Y. Chen, K. Ramchandran, P. Bartlett. Byzantine-robust distributed learning: towards optimal statistical rates. ICML, 2018.