118 citations · 324 across the 21 of their papers we have counts for
24 papers
Practical Feasibility of Gradient Inversion Attacks in Federated Learning
Viktor Valadi, Mattias Åkesson, Lucas Beerens +4
Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly strong reconstructions under favorable exp…
Toward efficient resource utilization at edge nodes in federated learning
Sadi Alawadi, Addi Ait-Mlouk, Salman Toor +1
Federated learning (FL) enables edge nodes to collaboratively contribute to constructing a global model without sharing their data. This is accomplished by devices computing local,…
Accelerating Fair Federated Learning: Adaptive Federated Adam
Li Ju, Tianru Zhang, Salman Toor +1
Federated learning is a distributed and privacy-preserving approach to train a statistical model collaboratively from decentralized data of different parties. However, when dataset…
FedQAS: Privacy-aware machine reading comprehension with federated learning
Addi Ait-Mlouk, Sadi Alawadi, Salman Toor +1
Machine reading comprehension (MRC) of text data is one important task in Natural Language Understanding. It is a complex NLP problem with a lot of ongoing research fueled by the r…
Efficient Hierarchical Storage Management Framework Empowered by Reinforcement Learning
Tianru Zhang, Salman Toor, Andreas Hellander
With the rapid development of big data and cloud computing, data management has become increasingly challenging. Over the years, a number of frameworks for data management and stor…
Scalable federated machine learning with FEDn
Morgan Ekmefjord, Addi Ait-Mlouk, Sadi Alawadi +5
Federated machine learning has great promise to overcome the input privacy challenge in machine learning. The appearance of several projects capable of simulating federated learnin…