A Unified Coding Framework for Distributed Computing with Straggling Servers
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Abstract (arXiv)
We propose a unified coded framework for distributed computing with straggling servers, by introducing a tradeoff between "latency of computation" and "load of communication" for some linear computation tasks. We show that the coded scheme of [1]-[3] that repeats the intermediate computations to create coded multicasting opportunities to reduce communication load, and the coded scheme of [4], [5] that generates redundant intermediate computations to combat against straggling servers can be viewed as special instances of the proposed framework, by considering two extremes of this tradeoff: minimizing either the load of communication or the latency of computation individually. Furthermore, the latency-load tradeoff achieved by the proposed coded framework allows to systematically operate at any point on that tradeoff to perform distributed computing tasks. We also prove an information-theoretic lower bound on the latency-load tradeoff, which is shown to be within a constant multiplicative gap from the achieved tradeoff at the two end points.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Sep 2016.
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Citing papers per year: 2021: 5; 2022: 2; 2023: 0; 2024: 0; 2025: 1.
8 papers.
- 2021CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learningcited by 20
- 2021List-Decodable Coded Computing: Breaking the Adversarial Toleration Barriercited by 4
- 2021Coding for Straggler Mitigation in Federated Learningcited by 4
- 2021Coded-InvNet for Resilient Prediction Serving Systemscited by 2
- 2021Coded Computation across Shared Heterogeneous Workers with Communication Delaycited by 1
- 2025FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learningcited by 1
- 2022Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff for Resilient, Secure, and Private Computationcited by 0
- 2022Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updatescited by 0
- 2025FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning9 Sep 2025
- 2022Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updates3 May 2022
- 2022Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff for Resilient, Secure, and Private Computation24 Apr 2022
- 2021CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning16 Dec 2021
- 2021Coding for Straggler Mitigation in Federated Learning30 Sep 2021
- 2021Coded Computation across Shared Heterogeneous Workers with Communication Delay23 Sep 2021
- 2021Coded-InvNet for Resilient Prediction Serving Systems11 Jun 2021
- 2021List-Decodable Coded Computing: Breaking the Adversarial Toleration Barrier27 Jan 2021