mlsanityData to 14 Oct 2026

A Communication Efficient Collaborative Learning Framework for Distributed Features

arXiv 1912.11187 ↗First posted 24 Dec 2019cs.LG cs.AI stat.MLcited by 21

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Abstract (arXiv)

We introduce a collaborative learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters. In particular, we propose a Federated Stochastic Block Coordinate Descent (FedBCD) algorithm, in which each party conducts multiple local updates before each communication to effectively reduce the number of communication rounds among parties, a principal bottleneck for collaborative learning problems. We analyze theoretically the impact of the number of local updates and show that when the batch size, sample size, and the local iterations are selected appropriately, within T iterations, the algorithm performs O(√T) communication rounds and achieves some O(1/√T) accuracy (measured by the average of the gradient norm squared). The approach is supported by our empirical evaluations on a variety of tasks and datasets, demonstrating advantages over stochastic gradient descent (SGD) approaches.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Dec 2019.

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Citing papers per year: 2021: 6; 2022: 7; 2023: 7; 2024: 0; 2025: 1.

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  1. 2022A Survey on Heterogeneous Federated Learningcited by 42
  2. 2021FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learningcited by 39
  3. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  4. 2022Accelerated Federated Learning with Decoupled Adaptive Optimizationcited by 23
  5. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  6. 2021AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilizationcited by 16
  7. 2022Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updatescited by 15
  8. 2023FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learningcited by 15
  9. 2021PIVODL: Privacy-preserving vertical federated learning over distributed labelscited by 9
  10. 2022Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithmcited by 9
  11. 2021GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborationscited by 8
  12. 2021Multi-Tier Federated Learning for Vertically Partitioned Datacited by 7
9 more
  1. 2023FedDebug: Systematic Debugging for Federated Learning Applicationscited by 6
  2. 2023Eliminating Label Leakage in Tree-Based Vertical Federated Learningcited by 6
  3. 2023Online Vertical Federated Learning for Cooperative Spectrum Sensingcited by 6
  4. 2021Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Ownerscited by 5
  5. 2022Hijack Vertical Federated Learning Models As One Partycited by 5
  6. 2022Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenariocited by 2
  7. 2023Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimizationcited by 2
  8. 2023TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenancecited by 1
  9. 2025Scaling Probabilistic Circuits via Data Partitioningcited by 1