# Resource Allocation for Compression-aided Federated Learning with High Distortion Rate

arXiv: https://arxiv.org/abs/2206.06976 · HTML: https://mlsanity.com/paper/2206.06976/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 2 Jun 2022.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Scheduling Policies for Federated Learning in Wireless Networks](https://mlsanity.com/paper/1908.06287/) (2019)
- [Ternary Compression for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2003.03564/) (2020)
- [Fusion of Federated Learning and Industrial Internet of Things: A Survey](https://mlsanity.com/paper/2101.00798/) (2021)
- [Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation](https://mlsanity.com/paper/2102.02946/) (2021)
- [Federated Learning over Wireless IoT Networks with Optimized Communication and Resources](https://mlsanity.com/paper/2110.11775/) (2021)
- [HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks](https://mlsanity.com/paper/2204.06760/) (2022)

## Built on by (most cited first, 2 of 2)

- [Distortion Resilience for Goal-Oriented Semantic Communication](https://mlsanity.com/paper/2309.14587/) (2023)
- [Label driven Knowledge Distillation for Federated Learning with non-IID Data](https://mlsanity.com/paper/2209.14520/) (2022)

## Abstract (arXiv)

Recently, a considerable amount of works have been made to tackle the communication burden in federated learning (FL) (e.g., model quantization, data sparsification, and model compression). However, the existing methods, that boost the communication efficiency in FL, result in a considerable trade-off between communication efficiency and global convergence rate. We formulate an optimization problem for compression-aided FL, which captures the relationship between the distortion rate, number of participating IoT devices, and convergence rate. Following that, the objective function is to minimize the total transmission time for FL convergence. Because the problem is non-convex, we propose to decompose it into sub-problems. Based on the property of a FL model, we first determine the number of IoT devices participating in the FL process. Then, the communication between IoT devices and the server is optimized by efficiently allocating wireless resources based on a coalition game. Our theoretical analysis shows that, by actively controlling the number of participating IoT devices, we can avoid the training divergence of compression-aided FL while maintaining the communication efficiency.