Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates

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A Survey on Multi-Task Learning (2017, methodology): “In [160], to protect the privacy of data, a privacy-preserving distributed MTL method is proposed based on a privacy-preserving proximal gradient”
Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition (2017, background): “Only recently have MTL scenarios been investigated where the tasks are distributed [2, 13, 19, 21, 32, 34].”
Differentially Private Hypothesis Transfer Learning (2018, methodology): “avoiding the complicated iterative hypothesis training process and privacy budget composition in [12,41], eliminating the need for a trusted third party in [13]).”
Privacy-preserving Transfer Learning for Knowledge Sharing (2018, methodology): “A stochastic gradient descent among parties are used in (Rajkumar and Agarwal 2012), and then a multi-task learning method is proposed in (Xie et al. 2017).”
Personalized Federated Learning: An Attentive Collaboration Approach (2020, background): “Distributed multi-task learning [33, 34, 35, 36] tried to conduct multi-task learning when the data for each task is separately owned by different clients.”
Learning Transferable Knowledge Through Embedding Spaces (2019, background): “Only recently have MTL scenarios been investigated where the tasks are distributed [235; 249; 250; 251; 252; 253].”