# Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation

arXiv: https://arxiv.org/abs/2403.14905 · HTML: https://mlsanity.com/paper/2403.14905/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 50 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 27 of 27)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Differential Privacy as a Mutual Information Constraint](https://mlsanity.com/paper/1608.03677/) (2016)
- [Decentralized Federated Learning via Mutual Knowledge Transfer](https://mlsanity.com/paper/2012.13063/) (2020)
- [Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks](https://mlsanity.com/paper/2011.06223/) (2020)
- [Straggler Mitigation in Distributed Optimization Through Data Encoding](https://mlsanity.com/paper/1711.04969/) (2017)
- [Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks](https://mlsanity.com/paper/2209.15595/) (2022)
- [ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding](https://mlsanity.com/paper/1901.09671/) (2019)
- [Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks](https://mlsanity.com/paper/2212.07356/) (2022)
- [Coded Federated Learning](https://mlsanity.com/paper/2002.09574/) (2020)
- [Approximate Gradient Coding with Optimal Decoding](https://mlsanity.com/paper/2006.09638/) (2020)
- [Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise](https://mlsanity.com/paper/1902.04688/) (2019)
- [Gradient Coding with Clustering and Multi-message Communication](https://mlsanity.com/paper/1903.01974/) (2019)
- [Towards Verifiable Federated Learning](https://mlsanity.com/paper/2202.08310/) (2022)
- [With Great Dispersion Comes Greater Resilience: Efficient Poisoning Attacks and Defenses for Linear Regression Models](https://mlsanity.com/paper/2006.11928/) (2020)
- [Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms](https://mlsanity.com/paper/2009.01098/) (2020)
- [Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning](https://mlsanity.com/paper/2103.01206/) (2021)
- [Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design](https://mlsanity.com/paper/2211.04132/) (2022)
- [Efficient Sparse Least Absolute Deviation Regression with Differential Privacy](https://mlsanity.com/paper/2401.01294/) (2024)
- [Distributed Learning based on 1-Bit Gradient Coding in the Presence of Stragglers](https://mlsanity.com/paper/2403.14716/) (2024)
- [Multi-Domain Virtual Network Embedding Algorithm based on Horizontal Federated Learning](https://mlsanity.com/paper/2205.14665/) (2022)
- [Sparse and Private Distributed Matrix Multiplication with Straggler Tolerance](https://mlsanity.com/paper/2306.15134/) (2023)

## Built on by (most cited first, 1 of 1)

- [Coding-Enforced Resilient and Secure Aggregation for Hierarchical Federated Learning](https://mlsanity.com/paper/2601.17995/) (2026)

## Abstract (arXiv)

In this article, we address the problem of federated learning in the presence of stragglers. For this problem, a coded federated learning framework has been proposed, where the central server aggregates gradients received from the non-stragglers and gradient computed from a privacy-preservation global coded dataset to mitigate the negative impact of the stragglers. However, when aggregating these gradients, fixed weights are consistently applied across iterations, neglecting the generation process of the global coded dataset and the dynamic nature of the trained model over iterations. This oversight may result in diminished learning performance. To overcome this drawback, we propose a new method named adaptive coded federated learning (ACFL). In ACFL, before the training, each device uploads a coded local dataset with additive noise to the central server to generate a global coded dataset under privacy preservation requirements. During each iteration of the training, the central server aggregates the gradients received from the non-stragglers and the gradient computed from the global coded dataset, where an adaptive policy for varying the aggregation weights is designed. Under this policy, we optimize the performance in terms of privacy and learning, where the learning performance is analyzed through convergence analysis and the privacy performance is characterized via mutual information differential privacy. Finally, we perform simulations to demonstrate the superiority of ACFL compared with the non-adaptive methods.