FedProx
An optimization algorithm for federated learning that adds a proximal regularization term to each client's local objective function to limit deviation between local and global models. It is designed for heterogeneous data settings.
Timeline (last 90 days)
Means of mitigating problems caused by non-IID data include the proximal term constraint of FedProx, the control variate correction of SCAFFOLD, and personalized federated learning.
Federated convex optimization algorithms such as SCAFFOLD and FedProx correct client drift by introducing variance reduction or proximal regularization terms, and have established near-optimal communication complexity bounds.
A key prerequisite for techniques such as SCAFFOLD and FedProx is that the operator F itself is a gradient field of some function, allowing the construction of a Lyapunov potential via function value descent to prove convergence, whereas a general monotone operator does not possess such a potential function.
All Facts (3)
Means of mitigating problems caused by non-IID data include the proximal term constraint of FedProx, the control variate correction of SCAFFOLD, and personalized federated learning.
50%UnverifiedFederated convex optimization algorithms such as SCAFFOLD and FedProx correct client drift by introducing variance reduction or proximal regularization terms, and have established near-optimal communication complexity bounds.
50%UnverifiedA key prerequisite for techniques such as SCAFFOLD and FedProx is that the operator F itself is a gradient field of some function, allowing the construction of a Lyapunov potential via function value descent to prove convergence, whereas a general monotone operator does not possess such a potential function.
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