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Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction

arXiv 2202.05139 ↗First posted 10 Feb 2022cs.LGcited by 0

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Abstract (arXiv)

Vertical federated learning (VFL) aims to train models from cross-silo data with different feature spaces stored on different platforms. Existing VFL methods usually assume all data on each platform can be used for model training. However, due to the intrinsic privacy risks of federated learning, the total amount of involved data may be constrained. In addition, existing VFL studies usually assume only one platform has task labels and can benefit from the collaboration, making it difficult to attract other platforms to join in the collaborative learning. In this paper, we study the platform collaboration problem in VFL under privacy constraint. We propose to incent different platforms through a reciprocal collaboration, where all platforms can exploit multi-platform information in the VFL framework to benefit their own tasks. With limited privacy budgets, each platform needs to wisely allocate its data quotas for collaboration with other platforms. Thereby, they naturally form a multi-party game. There are two core problems in this game, i.e., how to appraise other platforms' data value to compute game rewards and how to optimize policies to solve the game. To evaluate the contributions of other platforms' data, each platform offers a small amount of "deposit" data to participate in the VFL. We propose a performance estimation method to predict the expected model performance when involving different amount combinations of inter-platform data. To solve the game, we propose a platform negotiation method that simulates the bargaining among platforms and locally optimizes their policies via gradient descent. Extensive experiments on two real-world datasets show that our approach can effectively facilitate the collaborative exploitation of multi-platform data in VFL under privacy restrictions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 47 references that are in the map. First posted on arXiv 10 Feb 2022.

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26 of 47 references.

References · 26

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
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  6. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  7. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
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  12. 2019Measure Contribution of Participants in Federated Learningcited by 79
14 more
  1. 2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
  2. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 52
  3. 2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
  4. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45
  5. 2019A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regressioncited by 38
  6. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  7. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  8. 2019Interpret Federated Learning with Shapley Valuescited by 33
  9. 2020Asymmetrical Vertical Federated Learningcited by 26
  10. 2020Large-Scale Secure XGB for Vertical Federated Learningcited by 26
  11. 2018Entity Resolution and Federated Learning get a Federated Resolutioncited by 23
  12. 2018Joint Service Pricing and Cooperative Relay Communication for Federated Learningcited by 22
  13. 2022Fair and efficient contribution valuation for vertical federated learningcited by 18
  14. 2020Predictive and Causal Implications of using Shapley Value for Model Interpretationcited by 16

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