• Corpus ID: 235458306

Optimality and Stability in Federated Learning: A Game-theoretic Approach

@article{Donahue2021OptimalityAS,
  title={Optimality and Stability in Federated Learning: A Game-theoretic Approach},
  author={Kate Donahue and Jon M. Kleinberg},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.09580}
}
Federated learning is a distributed learning paradigm where multiple agents, each only with access to local data, jointly learn a global model. There has recently been an explosion of research aiming not only to improve the accuracy rates of federated learning, but also provide certain guarantees around social good properties such as total error. One branch of this research has taken a game-theoretic approach, and in particular, prior work has viewed federated learning as a hedonic game, where… 

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References

SHOWING 1-10 OF 42 REFERENCES
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
TLDR
A framework for incentiveaware learning and data sharing in federated learning is introduced and stable and envy-free equilibria capture notions of collaboration in the presence of agents interested in meeting their learning objectives while keeping their own sample collection burden low are captured.
Incentive Mechanism Design for Federated Learning: Hedonic Game Approach
TLDR
The Nash-stable set is proposed which determines the family of hedonic games possessing at least one Nash- stable partition, and the conditions of non-emptiness of the Nash- unstable set are analyzed.
Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation
TLDR
This work derives exact expected MSE values for problems in linear regression and mean estimation and uses these values to analyze the resulting game in the framework of hedonic game theory; it constructively shows that there always exists a stable partition of players into coalitions.
An Incentive Mechanism for Federated Learning in Wireless Cellular Networks: An Auction Approach
TLDR
This paper considers a FL system that involves one base station (BS) and multiple mobile users, and proposes the primal-dual greedy auction mechanism, which can guarantee three economic properties, namely, truthfulness, individual rationality and efficiency.
A Game-Theoretic Approach to Coalition Formation in Green Cloud Federations
TLDR
An algorithm is devised, based on cooperative game theory, that can be readily implemented in a distributed fashion, and that allows a set of CPs to cooperatively set up their federations in such a way that their individual profit is increased with respect to the case in which they work in isolation.
A game-theoretic approach to coalition formation in fog provider federations
TLDR
An algorithm is proposed, based on cooperative game theory, that enables each FIP to decide with whom to cooperate in order to increase its profits, and the effectiveness of the proposed algorithm is demonstrated through an experimental evaluation considering various workload intensities.
Fair Resource Allocation in Federated Learning
TLDR
This work proposes q-Fair Federated Learning (q-FFL), a novel optimization objective inspired by fair resource allocation in wireless networks that encourages a more fair accuracy distribution across devices in federated networks.
The price of stability for network design with fair cost allocation
TLDR
It is established that the fair cost allocation protocol is in fact a useful mechanism for inducing strategic behavior to form near-optimal equilibria, and its results are extended to cases in which users are seeking to balance network design costs with latencies in the constructed network.
The Stability of Hedonic Coalition Structures
TLDR
This work considers the partitioning of a society into coalitions in purely hedonic settings, and shows that if coalitions can be ordered according to some characteristic over which players have single-peaked preferences, then there exists an individually stable coalition partition.
Game of Gradients: Mitigating Irrelevant Clients in Federated Learning
TLDR
A principled approach is followed to address the above FRCS problems and a new federated learning method using the Shapley value concept from cooperative game theory is developed, which is presented as a cooperative game involving the gradients shared by the clients.
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