In this work, we propose a fast adaptive federated meta-learning (FAM)
framework for collaboratively learning a single global model, which can then be
personalized locally on individual clients. Federated learning enables multiple
clients to collaborate to train a model without sharing data. Clients with
insufficient data or data diversity participate in federated learning to learn
a model with superior performance. Nonetheless, learning suffers when data
distributions diverge. There is a need to learn a global model that can be
adapted using client's specific information to create personalized models on
clients is required. MRI data suffers from this problem, wherein, one, due to
data acquisition challenges, local data at a site is sufficient for training an
accurate model and two, there is a restriction of data sharing due to privacy
concerns and three, there is a need for personalization of a learnt shared
global model on account of domain shift across client sites. The global model
is sparse and captures the common features in the MRI. This skeleton network is
grown on each client to train a personalized model by learning additional
client-specific parameters from local data. Experimental results show that the
personalization process at each client quickly converges using a limited number
of epochs. The personalized client models outperformed the locally trained
models, demonstrating the efficacy of the FAM mechanism. Additionally, the
sparse parameter set to be communicated during federated learning drastically
reduced communication overhead, which makes the scheme viable for networks with
limited resources.Comment: 13 Pages, 1 figur