TMN: An Efficient Robust Aggregator for Federated Learning

This repository contains the source code for MICAD2023 Publication "TMN: An Efficient Robust Aggregator for Federated Learning"

The Base code was cloned from here

Abstract

The collaboration of multiple organizations, such as hospitals, with access to data, can expedite the training process, resulting in superior machine learning models with increased data availability. However, the sensitivity of medical data poses challenges to information sharing without compromising privacy and confidentiality. Federated Learning (FL) offers a promising solution by enabling collaborative training through a data-sharing-free approach. Nevertheless, a large number of FL aggregation algorithms assume clients are honest, leaving the global model vulnerable to poisoning attacks. Approaches to safeguard against such attacks often add high computational costs, making them unsuitable for practical applications. In this work, we propose a robust aggregation rule, named Trimmed-Median Neighbourhood, for Byzantine-tolerant machine learning, offering computational efficiency and resilience to various attacks. Our method achieves up to a 2% improvement over the baseline and modified approaches in an adversarial attack setting on a non-IID data split from the HAM10000 dataset while maintaining low computational requirements.

Experimental Settings

No Attack: 0 Byzantine Clients

Attack 1: 3 Byzantine Clients

• Clinet 1: Random Noise
• Clinet 2: Random Noise
• Clinet 3: Random Noise

Attack 2: 4 Byzantine Clients

• Clinet 1: Random Noise
• Clinet 2: Random Noise
• Clinet 3: 100 X Scaling
• Clinet 4: -1 X Scaling

Install dependencies

pip install -r requirements.txt

Download Dataset

The HAM10000 dataset can be used from here

Adding Dataset Path

Update the base_dir path in skinCancer_drich function in the dataset.py

Running Experiments

Run All Experimenets in No Attack Setting

bash run_no_attack.sh

Run All Experimenets in Attack-1 Setting

bash run_attack_1.sh

Run All Experimenets in Attack-2 Setting

bash run_attack_2.sh

Run Scaling Client Experimetns

bash run_client_scaling.sh

Results

Please run the results.ipynb to view plots and tables for all the experiments