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Client Adaptation improves Federated Learning with Simulated Non-IID Clients
We present a federated learning approach for learning a client adaptable...
07/09/2020 ∙ by Laura Rieger, et al. ∙
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Personalized Federated Learning using Hypernetworks
Personalized federated learning is tasked with training machine learning...
03/08/2021 ∙ by Aviv Shamsian, et al. ∙
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Active Federated Learning
Federated Learning allows for population level models to be trained with...
09/27/2019 ∙ by Jack Goetz, et al. ∙
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Decentralized Federated Learning via Mutual Knowledge Transfer
In this paper, we investigate the problem of decentralized federated lea...
12/24/2020 ∙ by Chengxi Li, et al. ∙
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Federated Adversarial Domain Adaptation
Federated learning improves data privacy and efficiency in machine learn...
11/05/2019 ∙ by Xingchao Peng, et al. ∙
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Personalized Federated Learning: An Attentive Collaboration Approach
For the challenging computational environment of IOT/edge computing, per...
07/07/2020 ∙ by Yutao Huang, et al. ∙
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Adaptive Distillation for Decentralized Learning from Heterogeneous Clients
This paper addresses the problem of decentralized learning to achieve a ...
08/18/2020 ∙ by Jiaxin Ma, et al. ∙
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Federated Unsupervised Representation Learning
10/18/2020 ∙ by Fengda Zhang, et al. ∙
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To leverage enormous unlabeled data on distributed edge devices, we formulate a new problem in federated learning called Federated Unsupervised Representation Learning (FURL) to learn a common representation model without supervision while preserving data privacy. FURL poses two new challenges: (1) data distribution shift (Non-IID distribution) among clients would make local models focus on different categories, leading to the inconsistency of representation spaces. (2) without the unified information among clients in FURL, the representations across clients would be misaligned. To address these challenges, we propose Federated Constrastive Averaging with dictionary and alignment (FedCA) algorithm. FedCA is composed of two key modules: (1) dictionary module to aggregate the representations of samples from each client and share with all clients for consistency of representation space and (2) alignment module to align the representation of each client on a base model trained on a public data. We adopt the contrastive loss for local model training. Through extensive experiments with three evaluation protocols in IID and Non-IID settings, we demonstrate that FedCA outperforms all baselines with significant margins.
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