Federated Learning with Heterogeneous Architectures using Graph HyperNetworks. (arXiv:2201.08459v1 [cs.LG])

Standard Federated Learning (FL) techniques are limited to clients with
identical network architectures. This restricts potential use-cases like
cross-platform training or inter-organizational collaboration when both data
privacy and architectural proprietary are required. We propose a new FL
framework that accommodates heterogeneous client architecture by adopting a
graph hypernetwork for parameter sharing. A property of the graph hyper network
is that it can adapt to various computational graphs, thereby allowing
meaningful parameter sharing across models. Unlike existing solutions, our
framework does …

arxiv federated learning graph learning