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Incentivizing Honesty among Competitors in Collaborative Learning
Florian Dorner · Nikola Konstantinov · Georgi Pashaliev · Martin Vechev

@
in 2nd ICML Workshop on New Frontiers in Adversarial Machine Learning »
Event URL: https://openreview.net/forum?id=wYqmPSKZ9S »

Collaborative learning techniques have the potential to enable training machine learning models that are superior to models trained on a single entity’s data. However, in many cases, potential participants in such collaborative schemes are competitors on a downstream task, such as firms that each aim to attract customers by providing the best recommendations. This can incentivize dishonest updates that damage other participants' models, potentially undermining the benefits of collaboration. In this work, we formulate a game that models such interactions and study two learning tasks within this framework: single-round mean estimation and multi-round SGD on strongly-convex objectives. For a natural class of player actions, we show that rational clients are incentivized to strongly manipulate their updates, thus preventing learning. We then propose mechanisms that incentivize honest communication and ensure learning quality comparable to full cooperation. Our work shows that explicitly modeling the incentives and actions of dishonest clients, rather than assuming them malicious, can enable strong robustness guarantees for collaborative learning.

Keywords: Optimization · federated learning · Economics · game theory · Mechanisms · Strategic Behavior

Author Information

Florian Dorner (Max Planck Institute for Intelligent Systems, Max-Planck Institute)
Nikola Konstantinov (INSAIT Institute, Sofia University)
Georgi Pashaliev (ETHZ - ETH Zurich)
Martin Vechev (ETH Zurich)

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