Direct Device Connection Assisted Federated Learning
Asynchronous Federated Learning
Distributed Joint Interface for 3D Object Detection
Consolidated Potential Requirements and KPIs
Conclusion and Recommendations
Scope and Objectives
Study use cases and identify functional and performance requirements for AIML operations using direct device connections.
Focus on:
Distributed AI inference
Decentralized model training
Gap analysis of existing 5G system mechanisms
Ultimate goal: Ensure the 5G system can effectively support AIML operations for applications like autonomous driving, robot remote control, and video recognition.
AIML Operations Overview
Types of AIML operations:
Splitting operations between AIML end-points
Distributing/sharing AIML models and data over the 5G system
Utilizing distributed or federated learning
Phase 2: Enhanced by direct device connections for efficient AIML processes
Proximity Based Work Task Off-loading
Concept: Offloading work from one device to another via direct device connections
Process:
Discover nearby device
Establish direct connection
Transfer intermediate data
Example: AlexNet model for image recognition
Mobile device offloads computation to a network server
Evaluation of layer-level computation and communication resource
Selection of split points for effective load management
Local AIML Model Split on Factory Robots
Use case: Autonomous robots assist human operators
Offload AIML model to another robot or service hosting environment when battery is low
Service Flows:
Sharing AIML models and sensors
Maintain efficiency through task offloading
Conditions: Proximity service support, continuous operation despite low battery
AIML Model Transfer Management
Goal: Relay or store AIML models for stable and reliable transfers using direct device connections
Components:
Operator MEC stores AIML models
Volunteer UEs assist in relaying models
Third party sets policies and requirements
Process:
Model transfer paths (direct communication)
Control mechanisms (manage transfer)
Volunteer UE selection (assist in transfer)
5G System Assisted Transfer Learning
Concept: Leverage pre-trained models for better training efficiency
Process:
Transfer model from UE1 to UE2 via 5G network
Fine-tune based on local data from UE2
Result: Reduced training time and enhanced performance
Direct Device Connection Assisted Federated Learning
Concept: Collaborative model training via direct device connections
Process:
Sharing training results locally
Decentralized aggregation of local model updates
Comparison:
Original FL vs. Lacy Metropolis algorithm
Enhanced efficiency and reduced communication overhead with Lacy Metropolis
Asynchronous Federated Learning
Concept: Devices report results when ready, without waiting for synchronization
Advantages: Resilient to varying Network conditions
Process:
Relay UEs determine QoS for member UEs
Efficient model training and data aggregation
Distributed Joint Interface for 3D Object Detection
Scenario: Multiple vehicles collaborate for 3D object detection
Share and process sensor data for enhanced accuracy