7 citations · 16 across the 5 of their papers we have counts for
5 papers · 1 filter
AdaBet: Gradient-free Layer Selection for Efficient Training of Deep Neural Networks
Irene Tenison, Soumyajit Chatterjee, Fahim Kawsar +1
To utilize pre-trained neural networks on edge and mobile devices, we often require efficient adaptation to user-specific runtime data distributions while operating under limited c…
Knowledge Distillation for Federated Learning: a Practical Guide
Alessio Mora, Irene Tenison, Paolo Bellavista +1
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting possibly sensitive raw data. The most used algorithms for FL are parameter-averagi…
Gradient Masked Averaging for Federated Learning
Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan +3
Federated learning (FL) is an emerging paradigm that permits a large number of clients with heterogeneous data to coordinate learning of a unified global model without the need to…
Gradient Masked Federated Optimization
Irene Tenison, Sreya Francis, Irina Rish
Federated Averaging (FedAVG) has become the most popular federated learning algorithm due to its simplicity and low communication overhead. We use simple examples to show that FedA…
Towards Causal Federated Learning For Enhanced Robustness and Privacy
Sreya Francis, Irene Tenison, Irina Rish
Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distributed training locally on participating de…