Federated learning is a promising learning method for data that is decentralized. For privacy reasons, the devices that own the data cannot share it without performing extra computations (such as encryption, secret sharing etc). Since the end devices have to compute local gradients over their dataset, then the presence of low processing nodes, referred to as stragglers, can outweigh the benefits of the parallelism. A central entity serves as an aggregation node, that aggregates the local gradients received from the end devices. Due to privacy reasons, the aggregation node shall not learn the dataset of a device from the local gradient it computes. SwiftAgg[1] is a scheme that allows straggler resiliency and secure aggregation. 

[1] - https://arxiv.org/pdf/2202.04169.pdf

- Information Theory