Systems and methods for federated learning using non-uniform quantization

A vehicle for training a machine learning model in a federated learning system, comprising:

• a controller programmed to:
• input a data point into the machine learning model including parameters quantized based on a first quantization level to obtain an output;
• quantize the output based on the first quantization level and a non-uniform quantization scheme;
• compute gradients with respect to parameters from a last layer to a first layer of the machine learning model based on the quantized output;
• quantize the gradients based on a second quantization level and the non-uniform quantization scheme;
• and
• update the machine learning model using the quantized gradients.

A system for training a machine learning model in a federated learning system, the system comprising:

• a server;
• and
• a plurality of edge nodes, each of the edge nodes comprising:
• a controller programmed to:
• input a data point into the machine learning model including parameters quantized based on a first quantization level to obtain an output;
• quantize the output based on the first quantization level and a non-uniform quantization scheme;
• compute gradients with respect to parameters from a last layer to a first layer of the machine learning model based on the quantized output;
• quantize the gradients based on a second quantization level and the non-uniform quantization scheme;
• and
• update the machine learning model using the quantized gradients.