FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models

Core Concepts

Introducing FedComLoc, a novel approach integrating compression techniques into federated learning to reduce communication costs effectively.

Abstract

The article discusses the challenges of high communication costs in Federated Learning (FL) and introduces FedComLoc as a solution. It combines compression techniques like model sparsity and quantization with efficient local training. The paper presents extensive experimental validation showing that FedComLoc significantly reduces communication costs while maintaining computational integrity. Various experiments are conducted on popular datasets like FedMNIST and FedCIFAR10 to evaluate the performance of FedComLoc in different scenarios, including sparsity ratios, data heterogeneity, quantization bits, and number of local iterations. The results demonstrate the effectiveness of FedComLoc in reducing communication overheads and improving training efficiency compared to baseline methods like FedAvg and Scaffold.

Stats

There exists c ∈ R s.t. ∥∇fi(x)∥ ≤ c for 1 ≤ i ≤ d. There exists c ∈ R s.t. 1/n Σ ∥∇fi(x)∥² ≤ c∥∇f(x)∥². Linear convergence has been proved when all functions fi are strongly convex. Total costs are a combined measurement of both communication costs and local computation cost. A communication round has unit cost while a local training round has cost τ.

Quotes

"Our primary objective is to solve the problem (ERM) and deploy the optimized global model to all clients." "To mitigate these costs, FL often employs Local Training (LT), a strategy where local parameters are updated multiple times before aggregation." "We proposed three variants of our algorithm addressing several key bottlenecks in FL." "Our evaluation comprises three distinct aspects."

Key Insights Distilled From

FedComLoc

by Kai ... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.09904.pdf

Deeper Inquiries

How can the integration of compression techniques like sparsity and quantization impact other areas beyond federated learning

The integration of compression techniques like sparsity and quantization in federated learning can have far-reaching impacts beyond just communication efficiency. One key area is model deployment, where compressed models require less storage space and computational resources, making them ideal for edge devices with limited capabilities. This can lead to faster inference times and reduced energy consumption, crucial for real-time applications like IoT devices or mobile platforms. Additionally, the use of compressed models can enhance privacy by reducing the amount of sensitive data that needs to be transmitted during inference.

What potential challenges or limitations might arise when implementing FedComLoc in real-world heterogeneous settings

Implementing FedComLoc in real-world heterogeneous settings may present several challenges and limitations. One major challenge is ensuring the compatibility of different client devices with the compression techniques used. Heterogeneous environments may have varying levels of computational power, memory capacity, and network bandwidth, which could impact the effectiveness of compression algorithms. Moreover, maintaining model accuracy while applying aggressive compression methods in diverse settings might require extensive hyperparameter tuning and optimization to balance performance trade-offs effectively.

How could advancements in compression techniques further enhance the efficiency of distributed training models

Advancements in compression techniques hold significant potential for further enhancing the efficiency of distributed training models beyond federated learning. By improving model sparsity through dynamic pruning strategies or exploring novel quantization approaches, researchers can reduce both communication costs and computational overheads across a wide range of distributed training scenarios. These advancements could lead to faster convergence rates, lower resource requirements on client devices, improved scalability for large-scale models, and enhanced robustness against noise or data heterogeneity commonly encountered in distributed settings.