Robust and Scalable Collaborative Learning via Pull-Based Epidemic Communication

Collaborative machine learning is vulnerable to adversarial behaviors during training. Existing defenses typically rely on central coordination or induce high communication costs. We introduce *Robust Pull-based Epidemic Learning (RPEL)*, a scalable and fully decentralized framework that achieves robustness without a central server. Unlike traditional methods, whose communication cost grows as $\mathcal{O}(n^2)$ with the number of nodes $n$, RPEL uses a pull-based epidemic communication scheme that scales as $\mathcal{O}(n \log n)$. By pulling model parameters from small, randomly selected subsets of peers, RPEL significantly lowers the number of required messages while preserving convergence guarantees with high probability. Experimental results demonstrate that RPEL indeed tolerates attacks, attains accuracy comparable with all-to-all communication, and scales efficiently to large networks.