mlsanityData to 14 Oct 2026

GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations

arXiv 2106.01425 ↗First posted 2 Jun 2021cs.LGcited by 8

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Abstract (arXiv)

Collaborations among multiple organizations, such as financial institutions, medical centers, and retail markets in decentralized settings are crucial to providing improved service and performance. However, the underlying organizations may have little interest in sharing their local data, models, and objective functions. These requirements have created new challenges for multi-organization collaboration. In this work, we propose Gradient Assisted Learning (GAL), a new method for multiple organizations to assist each other in supervised learning tasks without sharing local data, models, and objective functions. In this framework, all participants collaboratively optimize the aggregate of local loss functions, and each participant autonomously builds its own model by iteratively fitting the gradients of the overarching objective function. We also provide asymptotic convergence analysis and practical case studies of GAL. Experimental studies demonstrate that GAL can achieve performance close to centralized learning when all data, models, and objective functions are fully disclosed.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 33 of its 73 references that are in the map. First posted on arXiv 2 Jun 2021.

Builds on

33 of 73 references.

References · 33

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21 more
  1. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
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Cited by year

Citing papers per year: 2023: 5; 2024: 1; 2025: 2.

8 papers.

  1. 2023Pruning Deep Neural Networks from a Sparsity Perspectivecited by 25
  2. 2025Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probingcited by 14
  3. 2023Demystifying Poisoning Backdoor Attacks from a Statistical Perspectivecited by 10
  4. 2023VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarkscited by 6
  5. 2025Vertical Federated Learning in Practice: The Good, the Bad, and the Uglycited by 4
  6. 2023A Framework for Incentivized Collaborative Learningcited by 2
  7. 2023Collaborative Distributed Machine Learningcited by 1
  8. 2024ColA: Collaborative Adaptation with Gradient Learningcited by 1