AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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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...
A coreset-based task selection approach that selects a weighted subset of tasks based on how diverse they are in gradient space, prioritizing the most informative and diverse tasks is proposed, guaranteeing a faster adaptation to unseen tasks while focusing training on the most relevant tasks.
This work addresses the question of how multiple clients collaboratively learn dynamical models in the presence of heterogeneity by posing the problem as a federated learning problem and characterize the tension between achievable performance and system heterogeneity.
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