Browsing by Author "Özfatura, E."
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FedADC: Accelerated federated learning with drift control
Özfatura, E.; Özfatura, Ahmet Kerem; Gündüz, D. (IEEE, 2021)Federated learning (FL) has become de facto framework for collaborative learning among edge devices with privacy concern. The core of the FL strategy is the use of stochastic gradient descent (SGD) in a distributed manner. ... -
Time-correlated sparsification for communication-efficient federated learning
Özfatura, E.; Özfatura, Ahmet Kerem; Gündüz, D. (IEEE, 2021)Federated learning (FL) enables multiple clients to collaboratively train a shared model, with the help of a parameter server (PS), without disclosing their local datasets. However, due to the increasing size of the trained ...
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