17 citations · 66 across the 12 of their papers we have counts for
6 papers · 1 filter
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout
Chen Dun, Mirian Hipolito, Chris Jermaine +2
Asynchronous learning protocols have regained attention lately, especially in the Federated Learning (FL) setup, where slower clients can severely impede the learning process. Here…
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Yae Jee Cho, Andre Manoel, Gauri Joshi +2
Federated learning (FL) enables edge-devices to collaboratively learn a model without disclosing their private data to a central aggregating server. Most existing FL algorithms req…
Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
Tae Jin Park, Kenichi Kumatani, Dimitrios Dimitriadis
Federated Learning is a fast growing area of ML where the training datasets are extremely distributed, all while dynamically changing over time. Models need to be trained on client…
Dynamic Gradient Aggregation for Federated Domain Adaptation
Dimitrios Dimitriadis, Kenichi Kumatani, Robert Gmyr +2
In this paper, a new learning algorithm for Federated Learning (FL) is introduced. The proposed scheme is based on a weighted gradient aggregation using two-step optimization to of…
Federated Transfer Learning with Dynamic Gradient Aggregation
Dimitrios Dimitriadis, Kenichi Kumatani, Robert Gmyr +2
In this paper, a Federated Learning (FL) simulation platform is introduced. The target scenario is Acoustic Model training based on this platform. To our knowledge, this is the fir…
Progressive Neural Networks for Transfer Learning in Emotion Recognition
John Gideon, Soheil Khorram, Zakaria Aldeneh +2
Many paralinguistic tasks are closely related and thus representations learned in one domain can be leveraged for another. In this paper, we investigate how knowledge can be transf…