US 12,591,804 B2 · Systems and methods for distributed learning for wireless edge dynamics

Abstract

The apparatus of an edge computing node, a system, a method and a machine-readable medium. The apparatus includes a processor to perform rounds of federated machine learning training including: processing client reports from a plurality of clients of the edge computing network; selecting a candidate set of clients from the plurality of clients for an epoch of the federated machine learning training; causing a global model to be sent to the candidate set of clients; and performing the federated machine learning training on the candidate set of clients. The processor may perform rounds of federated machine learning training including: obtaining coded training data from each of the selected clients; and performing machine learning training on the coded training data.

Claims25

Dependent claims

An apparatus of a first edge computing node to be operated in an edge computing network, the apparatus including an interconnect interface to connect the apparatus to one or more components of the first edge computing node, and a processor to:

decode a first message from a second edge computing node, the first message including information on a target data distribution for federated machine learning training;
determine the target data distribution from the first message;
encode a client report for transmission to the second edge computing node based on a divergence between a local data distribution of the first edge computing node and the target data distribution;
cause transmission of the client report to the second edge computing node;
decode a second message from the second edge computing node, the second message including information on a global model associated with a global epoch of the federated machine learning training;
and
update a local gradient at the first edge computing node based on the global model.

4 more elements

A non-transitory computer-readable storage medium comprising instructions to cause a first edge computing node, upon execution of the instructions by one or more processors of the first edge computing node, to perform operations including:

decoding a first message from a second edge computing node, the first message including information on a target data distribution for federated machine learning training;
determining the target data distribution from the first message;
encoding a client report for transmission to the second edge computing node based on a divergence between a local data distribution of the first edge computing node and the target data distribution;
causing transmission of the client report to the second edge computing node;
decoding a second message from the second edge computing node, the second message including information on a global model associated with a global epoch of the federated machine learning training;
encoding for transmission to the second edge computing node a local weight update corresponding to a weight update of the first edge computing node at global epoch t and local epoch e, the local weight update based on a gradient estimate for the first edge computing node at global epoch t and local epoch e;
and
updating a local gradient at the first edge computing node based on the global model.

5 more elements

A method to be performed at an first edge computing node in an edge computing network, the method including:

decoding a first message from a second edge computing node, the first message including information on a target data distribution for federated machine learning training;
determining the target data distribution from the first message;
encoding a client report for transmission to the second edge computing node based on a divergence between a local data distribution of the first edge computing node and the target data distribution;
causing transmission of the client report to the second edge computing node;
decoding a second message from the second edge computing node, the second message including information on a global model associated with a global epoch of the federated machine learning training;
and
updating a local gradient at the first edge computing node based on the global model.

4 more elements

Dependencies are inferred from the claim text.

Systems and methods for distributed learning for wireless edge dynamics

Life of this patent

Systems and methods for distributed learning for wireless edge dynamics

Life of this patent

Abstract

Claims25