The invention discloses a federal learning adaptive gradient quantification method, which comprises the following steps of: initializing a training sample and a local model of each working node, training the local model by using the training sample to obtain a local gradient, and quantifying the local gradient according to a quantification level obtained by each working node; uploading the local gradient to a parameter server for gradient aggregation, and transmitting an aggregation result back to each working node; each working node updating local model parameters by using the quantized aggregation gradient; judging whether the number of iterations meets a preset interval time threshold, if so, broadcasting the link state of each working node and timely adjusting the self quantization level, otherwise, entering an iterative training process, and ending training until a preset condition is met. According to the method, the quantized bits of the gradient are adaptively adjusted according to the real-time bandwidth of the node link, the stragler problem is effectively relieved, the bandwidth resource utilization rate is improved on the basis that the communication overhead task is reduced through a traditional quantization method, and more efficient federal learning training is completed.