13 citations · 20 across the 6 of their papers we have counts for
9 papers
Deep learning-based denoising streamed from mobile phones improves speech-in-noise understanding for hearing aid users
Peter Udo Diehl, Hannes Zilly, Felix Sattler +11
The hearing loss of almost half a billion people is commonly treated with hearing aids. However, current hearing aids often do not work well in real-world noisy environments. We pr…
Reward-Based 1-bit Compressed Federated Distillation on Blockchain
Leon Witt, Usama Zafar, KuoYeh Shen +3
The recent advent of various forms of Federated Knowledge Distillation (FD) paves the way for a new generation of robust and communication-efficient Federated Learning (FL), where…
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning
Felix Sattler, Tim Korjakow, Roman Rischke +1
Federated Distillation (FD) is a popular novel algorithmic paradigm for Federated Learning, which achieves training performance competitive to prior parameter averaging based metho…
Communication-Efficient Federated Distillation
Felix Sattler, Arturo Marban, Roman Rischke +1
Communication constraints are one of the major challenges preventing the wide-spread adoption of Federated Learning systems. Recently, Federated Distillation (FD), a new algorithmi…
Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements
Felix Sattler, Jackie Ma, Patrick Wagner +6
Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS…
Trends and Advancements in Deep Neural Network Communication
Felix Sattler, Thomas Wiegand, Wojciech Samek
Due to their great performance and scalability properties neural networks have become ubiquitous building blocks of many applications. With the rise of mobile and IoT, these models…
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints
Felix Sattler, Klaus-Robert Müller, Wojciech Samek
Federated Learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints. Albeit it's popularit…
Robust and Communication-Efficient Federated Learning from Non-IID Data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller +1
Federated Learning allows multiple parties to jointly train a deep learning model on their combined data, without any of the participants having to reveal their local data to a cen…
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller +1
Currently, progressively larger deep neural networks are trained on ever growing data corpora. As this trend is only going to increase in the future, distributed training schemes a…