Add to ORKGWe present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS). At every communication round to the PS, each client computes a local consensus of the updates from its neighboring clients and eventually transmits a weighted average of its own update and those of its neighbors to the PS. We appropriately optimize these averaging weights to ensure that the global update at the PS is unbiased and to reduce the variance of the global update at the PS, consequently improving the rate of convergence. Numerical simulations substantiate our theoretical claims and demonstrate settings with intermittent connectivity between the clients and the PS, w...
This paper studies asynchronous Federated Learning (FL) subject to clients' individual arbitrary com...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by ...
We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying...
Intermittent client connectivity is one of the major challenges in centralized federated edge learni...
Federated learning (FL) is emerging as a new paradigm for training a machine learning model in coope...
Federated Learning is a machine learning paradigm where we aim to train machine learning models in a...
There are situations where data relevant to machine learning problems are distributed across multipl...
Many of the machine learning tasks rely on centralized learning (CL), which requires the transmissio...
Federated learning (FL) is a technique for distributed machine learning (ML), in which edge devices ...
Federated learning is an approach to distributed machine learning where a global model is learned by...
Decentralized learning algorithms empower interconnected devices to share data and computational res...
A recent emphasis of distributed learning research has been on federated learning (FL), in which mod...
Federated Averaging (FEDAVG) has emerged as the algorithm of choice for federated learning due to it...
Federated learning (FL) aims to minimize the communication complexity of training a model over heter...
This paper studies asynchronous Federated Learning (FL) subject to clients' individual arbitrary com...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by ...
We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying...
Intermittent client connectivity is one of the major challenges in centralized federated edge learni...
Federated learning (FL) is emerging as a new paradigm for training a machine learning model in coope...
Federated Learning is a machine learning paradigm where we aim to train machine learning models in a...
There are situations where data relevant to machine learning problems are distributed across multipl...
Many of the machine learning tasks rely on centralized learning (CL), which requires the transmissio...
Federated learning (FL) is a technique for distributed machine learning (ML), in which edge devices ...
Federated learning is an approach to distributed machine learning where a global model is learned by...
Decentralized learning algorithms empower interconnected devices to share data and computational res...
A recent emphasis of distributed learning research has been on federated learning (FL), in which mod...
Federated Averaging (FEDAVG) has emerged as the algorithm of choice for federated learning due to it...
Federated learning (FL) aims to minimize the communication complexity of training a model over heter...
This paper studies asynchronous Federated Learning (FL) subject to clients' individual arbitrary com...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by ...