3 research outputs found
FAM: fast adaptive federated meta-learning
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
Equitable-FL: Federated Learning with Sparsity for Resource-Constrained Environment
In Federated Learning, model training is performed across multiple computing
devices, where only parameters are shared with a common central server without
exchanging their data instances. This strategy assumes abundance of resources
on individual clients and utilizes these resources to build a richer model as
user's models. However, when the assumption of the abundance of resources is
violated, learning may not be possible as some nodes may not be able to
participate in the process. In this paper, we propose a sparse form of
federated learning that performs well in a Resource Constrained Environment.
Our goal is to make learning possible, regardless of a node's space, computing,
or bandwidth scarcity. The method is based on the observation that model size
viz a viz available resources defines resource scarcity, which entails that
reduction of the number of parameters without affecting accuracy is key to
model training in a resource-constrained environment. In this work, the Lottery
Ticket Hypothesis approach is utilized to progressively sparsify models to
encourage nodes with resource scarcity to participate in collaborative
training. We validate Equitable-FL on the , , and
benchmark datasets, as well as the data and the
datasets. Further, we examine the effect of sparsity on performance, model size
compaction, and speed-up for training. Results obtained from experiments
performed for training convolutional neural networks validate the efficacy of
Equitable-FL in heterogeneous resource-constrained learning environment.Comment: 12 pages, 7 figure
Deep phenotyping and genomic data from a nationally representative study on dementia in India
The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is a nationally representative in-depth study of cognitive aging and dementia. We present a publicly available dataset of harmonized cognitive measures of 4,096 adults 60 years of age and older in India, collected across 18 states and union territories. Blood samples were obtained to carry out whole blood and serum-based assays. Results are included in a venous blood specimen datafile that can be linked to the Harmonized LASI-DAD dataset. A global screening array of 960 LASI-DAD respondents is also publicly available for download, in addition to neuroimaging data on 137 LASI-DAD participants. Altogether, these datasets provide comprehensive information on older adults in India that allow researchers to further understand risk factors associated with cognitive impairment and dementia.Peer reviewe