FLAME

This repo holds the source code and scripts for reproducing the key experiments of our paper:

"On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness".

Authors: Shengkun Zhu, Jinshan Zeng, Sheng Wang, Yuan Sun, Xiaodong Li, Yuan Yao, Zhiyong Peng.

This repository is built based on PyTorch.

Datasets and Models

Datesets	# of samples	ref.	Models
Mnist	70,000	LeCun et al.	MLP
Fmnist	70,000	Xiao et al.	MLP
Mmnist	58,954	Kaggle	CNN1
Cifar10	60,000	Krizhevsky et al.	CNN
Femnist	382,705	Leaf	CNN2

Start

The default values for various parameters parsed to the experiment are given in options.py. Details are given on some of those parameters:

• framework: five personalized federated learning frameworks.

• partition: six data partitioning schemes.

• num_users: number of users.

• q: number of data shards of each user.

• model: SVM, MLP, MLR, CNN for choices.

• dataset: four datasets for choices.

• strategy: client selection strategy.

• frac_candidates: fraction of clients candidates, c/m in our paper.

• frac: fraction of clients, s/m in our paper.

• optimizer: type of optimizer, default sgd.

• momentum: sgd momentum, default 0.

• epochs: number of communication rounds.

• local_ep: the number of local iterations.

• local_bs: local batch size.

• lr: learning rate.

• mu: hyperparameter in regularization term.

• Lambda: hyperparameter in Moreau envelope.

• rho: hyperparameter in penalty term.

• iid: data distribution, 0 for non-iid.

• seed: random seed.

• eta: learning rate for the global model in pFedMe.

• eta2: learning rate for the global model in ditto.