Horizontal Federated Learning Frameworks: A Literature Study

Abstract

Federated Learning (FL)[1] is a type of distributed machine learning that allows the owners of the training data to preserve their privacy while still be- ing able to collectively train a model. FL is a new area in research and several chal- lenges reagarding privacy and communication cost still need to be overcome. Gradient leakage[1], for example is the possibility of partially reconstruct- ing the private data of a participant based on the weight gradients they send over the network dur- ing FL, which poses a great risk for privacy. Miti- gations against this problem lead to an increase in the computational complexity of the scheme or af- fect the performance of the resulting fully trained model. Other issues regarding trusting the central server or the clients, or accounting for clients that loose connection or drop out during training also exist. This paper is a literature survey about frameworks for Horizontal Federated Learning (HFL), which is a subset of Federated Learning, in which all clients have the same type of data (same set of features). The survey presents a summary of how 7 different Horizontal Federated Learning frameworks work, and compares them in terms of their performance and the security guarantees they provide. More- over, a summary of how each of the studied frame- works resolves the trade-offs among data privacy, framework performance and resulting model per- formance is also provided. Based on the studied frameworks it is concluded that the privacy and performance issues of HFL still need to be researched. Suggestions for future re- search topics are also provided.