Federated Learning Component Management via Transport Blocks
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Solution Overview
Problem
Current wireless communication technologies face challenges in efficiently managing machine learning components in federated learning, particularly in transmitting and receiving updates across wireless communication protocol stacks, which can lead to unreliable network performance due to the large size of machine learning components and updates.
Innovation Solution
The method involves a client device receiving a machine learning component from a server device, locally training it, and transmitting updates using multiple transport blocks via the lower layers of the wireless communication protocol stack, while the server device receives and manages these updates using both lower and upper layers of the protocol stack, facilitating more reliable network resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning components and updates are transmitted using traditional wireless communication protocols, then the updates can be sent over the network, but the large size of machine learning components leads to unreliable network performance and inefficient resource usage
Solution Approach 1:
The patent segments machine learning components and updates into multiple transport blocks for transmission over the wireless network. This segmentation allows the large data to be divided into manageable chunks that can be reliably transmitted and reassembled, directly addressing the unreliability issue caused by large component sizes.
Solution Approach 2:
The patent introduces a new dimension of management by implementing specialized upper layer protocols specifically designed for machine learning component communication. This adds a new layer of control and optimization beyond traditional transport protocols, enabling more efficient handling of the large data volumes.
2Productivity
If machine learning component updates are transmitted as large data packets, then all information can be sent in fewer transmissions, but this leads to unreliable delivery and inefficient network resource usage
Solution Approach 1:
The patent divides large machine learning updates into multiple smaller transport blocks, enabling more reliable transmission through standardized wireless protocols while maintaining efficient resource usage. The segmented approach allows for better error handling and retransmission of individual blocks if needed.
Solution Approach 2:
The patent introduces upper layer protocols as intermediaries between the machine learning application and the physical transport layer. These intermediary protocols provide specialized handling, management, and optimization for machine learning data, bridging the gap between application requirements and transport capabilities.
3Adaptability or versatility
If traditional wireless protocol stacks are used for machine learning component transmission, then existing infrastructure can be utilized, but the protocol stack is not optimized for the specific requirements of federated learning
Solution Approach 1:
The patent creates upper layer protocols that provide universal functionality for machine learning component management while working across different wireless communication standards. This multi-functional approach allows the same protocol layer to handle various machine learning transmission requirements regardless of the underlying physical layer technology.
Solution Approach 2:
The patent implements dynamic protocol selection and configuration capabilities that adapt to specific federated learning requirements. The protocol stack can dynamically adjust parameters and behaviors based on the characteristics of the machine learning data being transmitted, providing optimization without requiring complete protocol redesign.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client device may receive, using at least one lower layer of a wireless communication protocol stack, a machine learning component from a server device. The client device may transmit, to the server device and using the at least one lower layer, an update associated with the machine learning component, wherein transmitting the update comprises transmitting a plurality of transport blocks. Numerous other aspects are described.