Search Results for author: Felix Sattler

Found 8 papers, 3 papers with code

Reward-Based 1-bit Compressed Federated Distillation on Blockchain

no code implementations27 Jun 2021 Leon Witt, Usama Zafar, KuoYeh Shen, Felix Sattler, Dan Li, Wojciech Samek

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 mere soft-labels are aggregated, rather than whole gradients of Deep Neural Networks (DNN) as done in previous FL schemes.

Federated Learning Knowledge Distillation

FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning

1 code implementation4 Feb 2021 Felix Sattler, Tim Korjakow, Roman Rischke, Wojciech Samek

Federated Distillation (FD) is a popular novel algorithmic paradigm for Federated Learning, which achieves training performance competitive to prior parameter averaging based methods, while additionally allowing the clients to train different model architectures, by distilling the client predictions on an unlabeled auxiliary set of data into a student model.

Federated Learning Unsupervised Pre-training

Communication-Efficient Federated Distillation

no code implementations1 Dec 2020 Felix Sattler, Arturo Marban, Roman Rischke, Wojciech Samek

Communication constraints are one of the major challenges preventing the wide-spread adoption of Federated Learning systems.

Federated Learning Image Classification +2

Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements

no code implementations22 Apr 2020 Felix Sattler, Jackie Ma, Patrick Wagner, David Neumann, Markus Wenzel, Ralf Schäfer, Wojciech Samek, Klaus-Robert Müller, Thomas Wiegand

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-CoV-2 pandemic.

Trends and Advancements in Deep Neural Network Communication

no code implementations6 Mar 2020 Felix Sattler, Thomas Wiegand, Wojciech Samek

Due to their great performance and scalability properties neural networks have become ubiquitous building blocks of many applications.

Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints

1 code implementation4 Oct 2019 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.

Federated Learning Multi-Task Learning

Robust and Communication-Efficient Federated Learning from Non-IID Data

1 code implementation7 Mar 2019 Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek

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 centralized server.

Federated Learning

Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication

no code implementations22 May 2018 Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek

A major issue in distributed training is the limited communication bandwidth between contributing nodes or prohibitive communication cost in general.

Binarization

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