6 citations · 11 across the 4 of their papers we have counts for
6 papers
FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients
Xinchi Qiu, Yan Gao, Lorenzo Sani +6
Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized. The deploym…
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning
Yasar Abbas Ur Rehman, Yan Gao, Pedro Porto Buarque de Gusmão +3
The ubiquity of camera-enabled devices has led to large amounts of unlabeled image data being produced at the edge. The integration of self-supervised learning (SSL) and federated…
Privacy in Multimodal Federated Human Activity Recognition
Alex Iacob, Pedro P. B. Gusmão, Nicholas D. Lane +5
Human Activity Recognition (HAR) training data is often privacy-sensitive or held by non-cooperative entities. Federated Learning (FL) addresses such concerns by training ML models…
Pollen: High-throughput Federated Learning Simulation via Resource-Aware Client Placement
Lorenzo Sani, Pedro Porto Buarque de Gusmão, Alex Iacob +5
Federated Learning (FL) is a privacy-focused machine learning paradigm that collaboratively trains models directly on edge devices. Simulation plays an essential role in FL adoptio…
Can Fair Federated Learning reduce the need for Personalisation?
Alex Iacob, Pedro P. B. Gusmão, Nicholas D. Lane
Federated Learning (FL) enables training ML models on edge clients without sharing data. However, the federated model's performance on local data varies, disincentivising the parti…
Secure Vertical Federated Learning Under Unreliable Connectivity
Xinchi Qiu, Heng Pan, Wanru Zhao +5
Most work in privacy-preserving federated learning (FL) has focused on horizontally partitioned datasets where clients hold the same features and train complete client-level models…