Federated learning model splitting and combining
AssigneeQUALCOMM Incorporated
InventorsTianyang BAI, Hua WANG, Junyi LI
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Abstract
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network node may receive, from a first user equipment (UE) and a second UE, one or more parameter updates associated with a first model for federated learning. The network node may transmit, to the first UE, a second model for federated learning that is based at least in part on the first model. The network node may transmit, to the second UE, a third model for federated learning that is based at least in part on the first model. Numerous other aspects are described.
Claims30
An apparatus for wireless communication at a network node, comprising:
- a memory;
- and
- one or more processors coupled to the memory, the one or more processors configured to:
- receive, from a first user equipment (UE) and a second UE, one or more parameter updates associated with a first model for federated learning;
- transmit, to the first UE, a second model for federated learning that is based at least in part on the first model;
- and
- transmit, to the second UE, a third model for federated learning that is based at least in part on the first model.
An apparatus for wireless communication at a network node, comprising:
- a memory;
- and
- one or more processors coupled to the memory, the one or more processors configured to:
- receive, from a first user equipment (UE), a first parameter update associated with a first model for federated learning;
- receive, from a second UE, a second parameter update associated with a second model for federated learning;
- and
- transmit, to the first UE and the second UE, a third model for federated learning that is based at least in part on the first model and the second model.
A method of wireless communication performed by a network node, comprising:
- receiving, from a first user equipment (UE) and a second UE, one or more parameter updates associated with a first model for federated learning;
- transmitting, to the first UE, a second model for federated learning that is based at least in part on the first model;
- and
- transmitting, to the second UE, a third model for federated learning that is based at least in part on the first model.
A method of wireless communication performed by a network node, comprising:
- receiving, from a first user equipment (UE), a first parameter update associated with a first model for federated learning;
- receiving, from a second UE, a second parameter update associated with a second model for federated learning;
- and
- transmitting, to the first UE and the second UE, a third model for federated learning that is based at least in part on the first model and the second model.
Dependencies are inferred from the claim text.