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2019

Motivating Workers in Federated Learning: A Stackelberg Game Perspective

Sarikaya, Yunus, Ercetin, Ozgur

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Due to the large size of the training data, distributed learning approaches such as federated learning have gained attention recently.

However, the convergence rate of distributed learning suffers from heterogeneous worker performance.
In this paper, we consider an incentive mechanism for workers to mitigate the delays in completion of each batch.
We analytically obtained equilibrium solution of a Stackelberg game.

Motivating Workers in Federated Learning: A Stackelberg Game Perspective · Around

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Y. Sun, J. Zhao, S. Zhou, and D. Gündüz, “Heterogeneous computation across heterogeneous workers,” CoRR , vol. abs/1904.07490, 2019. [Online]. Available: http://arxiv.org/abs/1904.07490
Original
1904
Earlier work this paper cites.
M. Bibinger, “Notes on the sum and maximum of independent exponentially distributed random variables with different scaleparameters,” CoRR , vol. abs/1307.3945, 2013. [Online]. Available: http://arxiv.org/abs/1307.3945
Original
2013
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A. Harlap, H. Cui, W. Dai, J. Wei, G. R. Ganger, P. B. Gibbons, G. A. Gibson, and E. P. Xing, “Addressing the straggler problem for iterative convergent parallel ml,” in ACM Symposium on Cloud Computing (SoCC) , Santa Clara, CA, USA, Oct. 2016, pp. 98–111
2016
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H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Arcas, “Communication-efficient learning of deep networks from decentralized data,” in International Conference on Artificial Intelligence and Statistics (AISTATS) , Fort Lauderdale, FL, USA, Apr. 2017
2017
Earlier work this paper cites.

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A. G. D. R. Tandon, Q. Lei and N. Karampatziakis, “Gradient coding: avoiding stragglers in distributed learning,” in Proc. Int. Conf. on Machine Learning , Sydney, Australia, Feb. 2017, pp. 3368–3376
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C. Karakus, Y. Sun, S. Diggavi, and W. Yin, “Straggler mitigation in distributed optimization through data encoding,” in Advances in Neural Information Processing Systems 30 (NIPS) , Long Beach, NY, USA, Dec. 2017, pp. 5440–5448
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K. Lee, M. Lam, R. Pedarsani, D. Papailiopoulos, and K. Ramchandran, “Speeding up distributed machine learning using codes,” IEEE Transactions on Information Theory , vol. 64, no. 3, pp. 1514–1529, 2018
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U. Mohammad and S. Sorour, “Adaptive task allocation for mobile edge learning,” CoRR , vol. abs/1811.03748, 2018. [Online]. Available: http://arxiv.org/abs/1811.03748
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2018
Cited alongside, same era.

Then

S. Feng, D. Niyato, P. Wang, D. I. Kim, and Y. Liang, “Joint service pricing and cooperative relay communication for federated learning,” CoRR , vol. abs/1811.12082, 2018. [Online]. Available: http://arxiv.org/abs/1811.12082
Original
2018
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S. Dutta, G. Joshi, S. Ghosh, P. Dube, and P. Nagpurkar, “Slow and stale gradients can win the race: Error-runtime trade-offs in distributed sgd,” CoRR , vol. abs/1803.01113, 2018. [Online]. Available: https://arxiv.org/abs/1803.01113
Original
2018
Later among the works it cites.
J. Zhang, X. Hu, Z. Ning, E. C. . Ngai, L. Zhou, J. Wei, J. Cheng, and B. Hu, “Energy-latency tradeoff for energy-aware offloading in mobile edge computing networks,” IEEE Internet of Things Journal , vol. 5, no. 4, pp. 2633–2645, Aug 2018
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Later among the works it cites.

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