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Found 9 papers, 3 papers with code
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints
Federated Learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints.
Robust and Communication-Efficient Federated Learning from Non-IID Data
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.
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning
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.
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication
A major issue in distributed training is the limited communication bandwidth between contributing nodes or prohibitive communication cost in general.
Trends and Advancements in Deep Neural Network Communication
Due to their great performance and scalability properties neural networks have become ubiquitous building blocks of many applications.
Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements
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.
Communication-Efficient Federated Distillation
Communication constraints are one of the major challenges preventing the wide-spread adoption of Federated Learning systems.
Reward-Based 1-bit Compressed Federated Distillation on Blockchain
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.
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV).
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