Distributed Machine Learning in Information Centric Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Centralized machine learning techniques face challenges in efficiently processing large volumes of geographically dispersed data from mobile edge nodes due to high communication overhead and user privacy concerns, which are addressed by Distributed Machine Learning (DML) methods like Federated Learning. However, these methods suffer from significant network overhead in connection-based networks, particularly in Information-Centric Networking (ICN) environments.
Innovation Solution
The implementation of an optimized ICN layer for DML, utilizing a local trusted node as a coordinator to select and manage edge participants, reduce redundant message transmissions, and ensure secure data processing, by employing geo-area-aware selection techniques, group Quality of Service (QoS) aware participant selection, and secure model download procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized machine learning techniques are used to process geographically dispersed data from mobile edge nodes, then data processing capability is improved, but communication overhead increases significantly and user privacy concerns arise
Solution Approach 1:
The patent segments the centralized machine learning system into distributed edge nodes that independently process data locally. Each edge node maintains local models and processes data in its geographic region, eliminating the need to transmit large volumes of raw data across the network to a central cloud server. This segmentation directly reduces communication overhead while maintaining data processing capability.
Solution Approach 2:
The patent introduces information-centric networking (ICN) layer as an intermediary between edge nodes and the machine learning system. The ICN layer named data packets and manages data routing, allowing edge nodes to efficiently share model updates and parameters without establishing traditional connection-based communication channels. This intermediary layer optimizes data transmission and reduces network overhead.
2Loss of energy
If distributed machine learning methods are implemented to reduce communication overhead, then network efficiency is improved, but network overhead in ICN environments increases
Solution Approach 1:
The patent implements a universal ICN layer that serves multiple functions simultaneously: it names and routes data packets, manages caching at intermediate network nodes, handles security authentication, and coordinates distributed learning operations. This multi-functional approach consolidates what would otherwise require separate system components, reducing overall network overhead despite the complexity of distributed operations.
Solution Approach 2:
The patent employs preliminary actions by pre-naming data packets with descriptive identifiers that encode routing information, data type, and priority levels. The ICN layer pre-establishes caching strategies and routing paths before actual data transmission occurs. This preliminary organization of data naming and routing reduces the overhead required during actual distributed learning operations.
Data Source
AI summary
Systems and techniques for distributed machine learning (DML) in an information centric network (ICN) are described herein. Finite message exchanges, such as those used in many DML exercises, may be efficiently implemented by treating certain data packets as interest packets to reduce overall network overhead when performing the finite message exchange. Further, network efficiency in DML may be improved achieved by using local coordinating nodes to manage devices participating in a distributed machine learning exercise. Additionally, modifying a round of DML training to accommodate available participant devices, such as by using a group quality of service metric to select the devices, or extending the round execution parameters to include additional devices, may have an impact on DML performance.