Federated Learning (FL), a distributed machine learning technique has recently experienced tremendous growth in popularity due to its emphasis on user data privacy. However, the distributed computations of FL can result in constrained communication and drawn-out learning processes, necessitating the client-server communication cost optimization. The ratio of chosen clients and the quantity of local training passes are two hyperparameters that have a significant impact on the performance of FL. Due to different training preferences across various applications, it can be difficult for FL practitioners to manually select such hyperparameters. In our research paper, we introduce FedAVO, a novel FL algorithm that enhances communication effectiveness by selecting the best hyperparameters leveraging the African Vulture Optimizer (AVO). Our research demonstrates that the communication costs associated with FL operations can be substantially reduced by adopting AVO for FL hyperparameter adjustment. Through extensive evaluations of FedAVO on benchmark datasets, we show that FedAVO achieves significant improvement in terms of model accuracy and communication round, particularly with realistic cases of Non- IID datasets. Our extensive evaluation of the FedAVO algorithm identifies the optimal hyperparameters that are appropriately fitted for the benchmark datasets, eventually increasing global model accuracy by 6% in comparison to the state-of-the-art FL algorithms (such as FedAvg, FedProx, FedPSO, etc.)
First, you need to install the project dependencies. To do this, navigate to the project directory and run the following command:
pip install -r requirements.txt
This will install all the necessary packages for the project.
To run the project, use the following command in your terminal:
Example:
python3 main.py --optimizer= fedavo \
--data_split=iid --num_rounds=400 \
--clients_per_round=10 --batch_size = 4\
--num_epochs=5 --tuning_epoch=3 --train_samples=500
This will execute the main.py file, which contains the main code for the project.
GUIDELINES: Integration of FedAVO is feasible with diverse federated learning approaches and adaptable to various datasets. By adjusting the problem space (Which can be located in fedavo.py, under optimizers folder), one can achieve optimal tuning.
problem = {
"fit_func":objective_function ,
"lb": [0.1, 0.0000000001,0.0000000001 ], //lower limit
"ub": [0.1,0.000000001 ,0.000000001], // upper limit
"minmax": "min"}
@authors
-
Md Zarif Hossain,
Graduate Research Assistant, Southern Illinois University, Carbondale -
Ahmed Imteaj,
Assistant Professor, Southern Illinois University, Carbondale
See our FedAVO paper for more details as well as all references.