Communication Efficient Federated Learning over Multiple Access Channels

Abstract

In this work, we study the problem of federated learning (FL), where distributed users aim to jointly train a machine learning model with the help of a parameter server (PS). In each iteration of FL, users compute local gradients, followed by transmission of the quantized gradients for subsequent aggregation and model updates at PS. One of the challenges of FL is that of communication overhead due to FL's iterative nature and large model sizes. One recent direction to alleviate communication bottleneck in FL is to let users communicate simultaneously over a multiple access channel (MAC), possibly making better use of the communication resources. In this paper, we consider the problem of FL learning over a MAC. In particular, we focus on the design of digital gradient transmission schemes over a MAC, where gradients at each user are first quantized, and then transmitted over a MAC to be decoded individually at the PS. When designing digital FL schemes over MACs, there are new opportunities to assign different amount of resources (such as rate or bandwidth) to different users based on a) the informativeness of the gradients at each user, and b) the underlying channel conditions. We propose a stochastic gradient quantization scheme, where the quantization parameters are optimized based on the capacity region of the MAC. We show that such channel aware quantization for FL outperforms uniform quantization, particularly when users experience different channel conditions, and when have gradients with varying levels of informativeness.

References in corpus (8)

• QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
• Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
• Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization
• On Analog Gradient Descent Learning over Multiple Access Fading Channels
• Distributed Mean Estimation with Limited Communication
• A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
• Federated Learning via Over-the-Air Computation
• Energy-Aware Analog Aggregation for Federated Learning with Redundant Data

Cited by in corpus (9)

• Over-the-Air Federated Learning from Heterogeneous Data
• On Analog Gradient Descent Learning over Multiple Access Fading Channels
• Communication-Efficient Edge AI: Algorithms and Systems
• Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints
• Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control
• Communication Efficient Federated Learning with Energy Awareness over Wireless Networks
• Linear Regression over Networks with Communication Guarantees
• MyMigrationBot: A Cloud-based Facebook Social Chatbot for Migrant Populations
• Wireless Distributed Edge Learning: How Many Edge Devices Do We Need?