Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning

Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning M. Yashwanth, G. K. Nayak, A. Singh, Yogesh Simmhan, A. Chakraborty
Indian Institute of Science, Visual Computing Lab

This repository contains the pytorch implementation for a novel Federated Learning (FL) regularization technique, Adaptive Self-Distillation (ASD). ASD addresses the "client-drift" problem in FL, where clients diverge towards local optima due to non-IID data distributions. By adaptively adjusting the regularization strength based on the global model's prediction entropy and client-data label distribution, ASD significantly improves the convergence speed and generalization performance of FL algorithms.

🚀 Main Results

Top-1 accuracy

Method	CIFAR-100			Tiny-Imagenet
	$\delta$ = 0.3	$\delta$ = 0.6	IID	$\delta$ = 0.3	$\delta$ = 0.6	IID
FedAvg	38.67	38.53	37.68	23.89	23.95	23.48
FedAvg+ASD (ours)	42.77	42.54	43.00	25.31	26.38	26.67
FedDyn	47.56	48.60	48.87	27.62	28.58	28.37
FedDyn+ASD (ours)	49.03	50.23	51.44	29.94	30.05	30.76
FedSpeed	47.39	48.27	49.01	28.60	29.33	29.62
FedSpeed+ASD (ours)	49.16	49.76	51.99	30.97	30.05	32.68

Note: Find results for other methods in Table 2 of the paper.

Ablation Results

Effect of Adaptive Weights on Distillation (CIFAR-100)

Method	Uniform weights	Adaptive weights
FedAvg+ASD	41.75	42.77
FedNTD+ASD	40.40	43.01
FedDisco+ASD	40.21	41.55
FedDyn+ASD	47.90	49.03

🔨 Setup

• CIFAR datasets are downloaded using torchvision.
• Imagenet-100 dataset is downloaded from here and reorganized into train and test folders.
• To prepare dataloader pickle files: Run command python tiny_imagenet.py <path_to_dataset_folder>. Change the degree of heterogeneity (rule_arg) and pickle output filename in the code.
• All the experiments should be configured using config.yml.
• To restart from a saved checkpoint: add --use_checkpoint 1 and --restart_round <round> to the command or configure these parameters in config.yml.

🧪 Running Experiments

• Run python main.py to train FedAvg algorithm on CIFAR-100 dataset across 100 clients for 500 rounds with the following setting:

dataset_name: CIFAR100
alg_name: FedAvg
n_client: 100
com_amount: 500
add_reg: 0 // To add ASD regularizer
dist_beta_kl: 1.0 // hyperparameter for the label imbalance loss
lamda: 0.0 // hyperparameter for regularizer
data_pkl: dir_0.3_cifar100_data.pkl // path to dataloader pickle file
use_checkpoint: 0 // or 1 if starting from a saved model checkpoint
restart_round: <restart_round> // restart from this round

• To add SAM/ASAM optimizer for FedSAM/FedASAM, use sam: 1 or asam: 1 accordingly.
• To run FedNTD, keep the settings as such:

alg_name: FedAvgReg
ntd: 1
uniform_distill: 1 // Change it to 0 to add the ASD regularizer on top of FedNTD

Note: add_reg should not be used to distinguish between FedAvg and FedAvg+ASD. Use FedAvgReg to add ASD regularizer in FedAvg.

• To run FedDisco: keep alg_name FedAvg and use disco: 1 in the config file. Use alg_name FedAvgReg to add ASD regularizer in FedDisco.

*Raise an issue if unable to run any experiment*

👍 Acknowledgement

This project is built upon the codebase of FedDyn. Thanks to all the contributors of this work. We would also like to acknowledge the work of Wu et al. (2022) for providing the foundational model architecture of Tiny-VIT.

📝 Citation

If you think this project is helpful, please feel free to cite our paper:

@article{
  anonymous2024adaptive,
  title={Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning},
  author={M. Yashwanth, G. K. Nayak, Arya Singh, Yogesh Simmhan, Anirban Chakraborty},
  journal={Submitted to Transactions on Machine Learning Research},
  year={2024},
  url={https://openreview.net/forum?id=K58n87DE4s},
}