Analog Protocol Stack for Federated Learning Gradient Transmission
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Solution Overview
Problem
Existing wireless communication networks, particularly those using New Radio (NR) technology, are not designed to efficiently process analog gradient data for federated learning, leading to inefficiencies in machine learning operations.
Innovation Solution
Introduce an architectural enhancement to the NR network with an analog protocol stack that enables the computation of uplink channel-based analog gradient sums, incorporating an analog protocol stack and a digital protocol stack to handle machine learning model gradients or weights, allowing transmission in either analog or digital format based on network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital protocol stack is used for ML data transmission, then reliability is improved, but communication overhead and latency increase
Solution Approach 1:
The system dynamically changes the transmission parameter from digital to analog domain based on network conditions. When many UEs participate in federated learning, the system switches to analog gradient transmission to reduce latency, while maintaining digital transmission for smaller groups to ensure reliability. This parameter switching resolves the contradiction between reliability and latency.
2Productivity
If analog gradient transmission is used, then communication efficiency is improved, but compatibility with existing NR protocols deteriorates
Solution Approach 1:
The system introduces an intermediary analog protocol stack that bridges the gap between traditional digital NR protocols and analog gradient transmission. This intermediary layer handles the conversion and coordination, allowing analog transmission to work within the existing NR framework without breaking compatibility, thus resolving the contradiction between efficiency and adaptability.
Solution Approach 2:
The system implements dynamic protocol selection where the transmission mode (analog or digital) is determined based on real-time network conditions such as the number of participating UEs. This dynamic adaptation allows the system to optimize for analog transmission when beneficial while maintaining fallback to digital protocols for compatibility, resolving the contradiction between efficiency and adaptability.
3Adaptability or versatility
If federated learning is implemented in NR networks, then machine learning capabilities are improved, but processing efficiency for analog gradient data deteriorates
Solution Approach 1:
The system segments the protocol stack into separate analog and digital components, with the analog protocol stack specifically designed for efficient gradient transmission. This segmentation allows ML capabilities to be added through the analog layer without interfering with the existing digital processing infrastructure, thus improving processing efficiency for analog gradient data while maintaining ML capabilities.
Data Source
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AI summary
A protocol stack architecture for processing machine learning (ML) data includes a ML layer to manage ML data communication with a network device. The ML layer is coupled to multiple ML training blocks, and ML and inference blocks for multiple neural networks, and an analog data communications stack coupled to the ML layer. The analog data communications stack has an upper media access control analog (MAC-A) layer coupled to the ML layer and configured to store data for each neural network, a lower MAC-A layer coupled to the upper MAC-A layer and configured to segment and reassemble analog ML data, and an analog physical layer coupled to the lower MAC-A layer and configured to communicate analog data with the network device. The architecture includes a digital data communications stack coupled to the ML layer and the lower MAC-A layer and configured to manage digital communications with the network device.