QUALCOMM Incorporated
TECHNIQUES FOR ADAPTIVE QUANTIZATION LEVEL SELECTION IN FEDERATED LEARNING

Abstract:

Methods, systems, and devices for wireless communications are described. To support adaptive quantization level selection in federated learning, a server may cause a base station to transmit an indication of a quantization level for a user equipment (UE) to use to compress gradient data output by a machine learning model. For example, the server may determine, for each UE of a set of UEs, a respective quantization level for respective gradient data that is output by a respective machine learning model at each UE. The server may transmit, to each UE via one or more base stations, first information for use as an input in the respective machine learning model and an indication of the respective quantization level. A UE may receive the first information and the indication and may transmit, to the server, compressed gradient data that is generated based on (e.g., using) the indicated quantization level.