Felix Sattler

TL;DR: In this paper, the authors propose sparse ternary compression (STC), a new compression framework that is specifically designed to meet the requirements of the federated learning environment, which extends the existing compression technique of top- $k$ gradient sparsification with a novel mechanism to enable downstream compression as well as ternarization and optimal Golomb encoding of the weight updates.

TL;DR: Sparse ternary compression (STC) is proposed, a new compression framework that is specifically designed to meet the requirements of the federated learning environment and advocate for a paradigm shift in federated optimization toward high-frequency low-bitwidth communication, in particular in the bandwidth-constrained learning environments.

TL;DR: Closed FL (CFL), a novel federated multitask learning (FMTL) framework, which exploits geometric properties of the FL loss surface to group the client population into clusters with jointly trainable data distributions, and comes with strong mathematical guarantees on the clustering quality.

TL;DR: Clustered FL (CFL) as discussed by the authors exploits geometric properties of the FL loss surface to group the client population into clusters with jointly trainable data distributions, which can be viewed as a postprocessing method that will always achieve greater or equal performance than conventional FL by allowing clients to arrive at more specialized models.

TL;DR: Sparse Binary Compression (SBC) as mentioned in this paper combines existing techniques of communication delay and gradient sparsification with a novel binarization method and optimal weight update encoding to push compression gains to new limits.