In many distributed and federated learning systems clients iteratively compute so-called gradient vectors based on their locally stored data and communicate them to a central entity. The gradient vectors are typically high dimensional, so transmitting them directly leads to undesirable amounts of data transmission, holding back the performance of the system.

To alleviate this issue, various gradient compression schemes have been proposed.

The student's task is to analyze and compare multiple proposed schemes based on their advantages and disadvantages. As a starting point, students can use [1, Section 3.2].

[1] S. Ouyang, D. Dong, Y. Xu, and L. Xiao, “Communication optimization strategies for distributed deep neural network training: A survey,” Journal of Parallel and Distributed Computing, vol. 149, pp. 52–65, Mar. 2021, doi: 10.1016/j.jpdc.2020.11.005.

We study the impact of different measures of fairness on the privacy guarantees for individual clients in federated learning.

We study the usage of channel codes for the setting of federated learning with over-the-air computation so that the sum of codewords over reals can be efficiently and reliably decoded at the federator to obtain the average of partial model updates.