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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 8: AAAI-22 Technical Tracks 8

ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems

February 1, 2023

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Authors

Mahdi Soleymani

University of Michigan


Ramy E. Ali

University of Southern California


Hessam Mahdavifar

University of Michigan


A. Salman Avestimehr

University of Southern California


DOI:

10.1609/aaai.v36i8.20809


Abstract:

Due to the surge of cloud-assisted AI services, the problem of designing resilient prediction serving systems that can effectively cope with stragglers and minimize response delays has attracted much interest. The common approach for tackling this problem is replication which assigns the same prediction task to multiple workers. This approach, however, is inefficient and incurs significant resource overheads. Hence, a learning-based approach known as parity model (ParM) has been recently proposed which learns models that can generate ``parities’’ for a group of predictions to reconstruct the predictions of the slow/failed workers. While this learning-based approach is more resource-efficient than replication, it is tailored to the specific model hosted by the cloud and is particularly suitable for a small number of queries (typically less than four) and tolerating very few stragglers (mostly one). Moreover, ParM does not handle Byzantine adversarial workers. We propose a different approach, named Approximate Coded Inference (ApproxIFER), that does not require training any parity models, hence it is agnostic to the model hosted by the cloud and can be readily applied to different data domains and model architectures. Compared with earlier works, ApproxIFER can handle a general number of stragglers and scales significantly better with the number of queries. Furthermore, ApproxIFER is robust against Byzantine workers. Our extensive experiments on a large number of datasets and model architectures show significant degraded mode accuracy improvement by up to 58% over ParM.

Topics: AAAI

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HOW TO CITE:

Mahdi Soleymani||Ramy E. Ali||Hessam Mahdavifar||A. Salman Avestimehr ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems Proceedings of the AAAI Conference on Artificial Intelligence (2022) 8342-8350.

Mahdi Soleymani||Ramy E. Ali||Hessam Mahdavifar||A. Salman Avestimehr ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems AAAI 2022, 8342-8350.

Mahdi Soleymani||Ramy E. Ali||Hessam Mahdavifar||A. Salman Avestimehr (2022). ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 8342-8350.

Mahdi Soleymani||Ramy E. Ali||Hessam Mahdavifar||A. Salman Avestimehr. ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.8342-8350.

Mahdi Soleymani||Ramy E. Ali||Hessam Mahdavifar||A. Salman Avestimehr. 2022. ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems. "Proceedings of the AAAI Conference on Artificial Intelligence". 8342-8350.

Mahdi Soleymani||Ramy E. Ali||Hessam Mahdavifar||A. Salman Avestimehr. (2022) "ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems", Proceedings of the AAAI Conference on Artificial Intelligence, p.8342-8350

Mahdi Soleymani||Ramy E. Ali||Hessam Mahdavifar||A. Salman Avestimehr, "ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems", AAAI, p.8342-8350, 2022.

Mahdi Soleymani||Ramy E. Ali||Hessam Mahdavifar||A. Salman Avestimehr. "ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.8342-8350.

Mahdi Soleymani||Ramy E. Ali||Hessam Mahdavifar||A. Salman Avestimehr. "ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 8342-8350.

Mahdi Soleymani||Ramy E. Ali||Hessam Mahdavifar||A. Salman Avestimehr. ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems. AAAI[Internet]. 2022[cited 2023]; 8342-8350.


ISSN: 2374-3468


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