Back to results
US 2023/0401452 A1Published application
Systems and methods for weight-agnostic federated neural architecture search
AssigneeArizona Board of Regents on behalf of Arizona State University
InventorsRida Bazzi, Om Thakkar
Life of this patent
Estimating the term…
Abstract
Systems and methods herein train weight-agnostic networks in a federated learning setting with orthogonal data distribution. Unlike traditional networks, weight-agnostic networks have a small size and can be trained using neural architecture search. The methods and systems described herein include sharing of a subset of networks between clients to allow federated learning for weight-agnostic networks in which clients do not have samples from all classes.
Claims8
A system, comprising:
- a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to:
- initialize, at the processor, a plurality of minimally connected networks;
- evaluate, at the processor, the plurality of minimally connected networks using a random sample from each respective class of a plurality of classes within a local dataset associated with a first client A;
- facilitate, at the processor, a validation exchange between one or more minimally connected networks of the plurality of minimally connected networks of the first client A and one or more minimally connected networks of the plurality of minimally connected networks of a second client B;
- assess, based on the validation exchange, a suitability of one or more minimally connected networks of the plurality of minimally connected networks of the first client A with respect to the second client B;
- and
- select one or more minimally connected networks of the plurality of minimally connected networks of the first client A to share with the second client B.
4 more elements
Dependencies are inferred from the claim text.