5.4 Algorithm - FedAdaGrad

Implement Federated Learning with the Adaptive optimization method AdaGrad (FedAdagrad) algorithm[1].

Parameters

• factors (str, optional) - aggregation weight mode (default: "weighted")
  • "weighted": weighted average based on the size of local datasets
  • "arithmetic": arithmetic average
  • "self-defined": weighted average according to user-defined weights
• lr (float, optional) - learning rate (default: 0.03)
• beta (float, optional) - coefficients used for computing running averages of gradient (default: 0)
• eps (float, optional) -  term added to the denominator to improve numerical stability (default: 1e-3)
• lr_decay_rate (float, optional) - learning rate decay factor (default: 1)
• lr_decay_step (int, optional) - period of learning rate decay (default: 100)
• bias_correction (bool, optional) - bias correction (default: False)

Reference

[1] Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecny, Sanjiv Kumar, and Hugh Brendan McMahan. Adaptive federated optimization. In International Conference on Learning Representations, ICLR, 2021

The user interface of FedAdaGrad algorithm: