mlsanityData to 14 Oct 2026

Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange

arXiv 2208.02856 ↗First posted 4 Aug 2022cs.LGcited by 5

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Abstract (arXiv)

Federated learning (FL) has been recognized as one of the most promising solutions for distributed machine learning (ML). In most of the current literature, FL has been studied for supervised ML tasks, in which edge devices collect labeled data. Nevertheless, in many applications, it is impractical to assume existence of labeled data across devices. To this end, we develop a novel methodology, Cooperative Federated unsupervised Contrastive Learning (CF-CL), for FL across edge devices with unlabeled datasets. CF-CL employs local device cooperation where data are exchanged among devices through device-to-device (D2D) communications to avoid local model bias resulting from non-independent and identically distributed (non-i.i.d.) local datasets. CF-CL introduces a push-pull smart data sharing mechanism tailored to unsupervised FL settings, in which, each device pushes a subset of its local datapoints to its neighbors as reserved data points, and pulls a set of datapoints from its neighbors, sampled through a probabilistic importance sampling technique. We demonstrate that CF-CL leads to (i) alignment of unsupervised learned latent spaces across devices, (ii) faster global convergence, allowing for less frequent global model aggregations; and (iii) is effective in extreme non-i.i.d. data settings across the devices.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 16 of its 25 references that are in the map. First posted on arXiv 4 Aug 2022.

Builds on

16 of 25 references.

References · 16

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  6. 2018Federated Learning with Non-IID Datacited by 1,108
  7. 2021Model-Contrastive Federated Learningcited by 766
  8. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  9. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  10. 2020Federated Unsupervised Representation Learningcited by 93
  11. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 51
  12. 2020Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networkscited by 42
4 more
  1. 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
  2. 2020Optimal Importance Sampling for Federated Learningcited by 20
  3. 2022Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clientscited by 20
  4. 2022Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networkscited by 18

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 2.

5 papers.

  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2023Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networkscited by 2
  3. 2024Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learningcited by 2
  4. 2023Coded Matrix Computations for D2D-enabled Linearized Federated Learningcited by 0
  5. 2024Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labelscited by 0