Performance comparison of different federated learning aggregation algorithms

Performance comparison of different federated learning aggregation algorithms: How does the performance of different federated learning aggregation algorithms compare to each other?

Katz, Roy (TU Delft Electrical Engineering, Mathematics and Computer Science)

Reinders, M.J.T. (mentor)
Garst, S.J.F. (mentor)
Chen, Lydia Y. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-28

Federated learning enables the construction of machine learning models, while adhering to privacy constraints and without sharing data between different devices. It is achieved by creating a machine learning model on each device that contains data, and then combining these models through an aggregation algorithm without sharing the data. Federated learning is currently a hot topic, and a lot of research has gone into implementing accurate aggregation algorithms. The original algorithm is FedAvg, and since then many different algorithms have been introduced. In this paper, I will compare the performance of five different aggregation algorithms: FedAvg, FedProx, FedYogi, FedMedian and q-FedAvg. The algorithms are compared on different data sets, namely MNIST and a kinase inhibition data set, as well as on different data distributions and number of clients. The experiments indicate that among these five algorithms, FedYogi achieves the best performance, both in terms of highest final accuracy as well as in terms of convergence rate.

Federated learning
Aggregation algorithms
Comparison
FedAvg
q-FedAvg
FedYogi
FedMedian
FedProx

http://resolver.tudelft.nl/uuid:6b7f0876-3f11-4095-aff3-2a59cf640250

Student theses

bachelor thesis

© 2023 Roy Katz