AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Robotic systems often exhibit unstable modes, along which small perturbations and disturbances can cause unbounded growth unless corrected through feedback. Controlling such systems from high-dimensional visual observations requires representations that preserve these modes. Joint-embedding predictive architectures (JE...
We study federated learning (FL) with adversarial clients, where the goal is to minimize the average loss of the honest (non-adversarial) clients without knowing their identity. Under heterogeneity, a single shared model parameter is statistically inappropriate: it cannot capture the distinct data-generating processes...
It is demonstrated that, for any $(f,\kappa)$-robust aggregator with coefficient $\kappa = O(f/n)$, where f is the number of adversarial clients and n is the total number of clients, convergence holds after an explicit sample burn-in.
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