Over-the-Air-Computation Based Federated Learning
Established and simulated Over-the-Air computation based federated learning model using MATLAB/Simulink
Overview
Developed a novel approach to federated learning using Over-the-Air (OTA) computation, implementing both basic AM modulation and advanced OFDM-based communication architectures in MATLAB/Simulink.
Theoretical Framework
Theoretical framework of analog Over-the-Air computation based Federated Learning, showing the mathematical principles and system design.
Basic Implementation
Simulink implementation using basic AM modulation/demodulation with 4 local devices, demonstrating the fundamental OTA computation concept.
OFDM-based Architecture
Left: OFDM transmitter architecture for OTA computation. Right: OFDM receiver design with built-in synchronization and channel estimation.
Technical Details
- Implemented two communication architectures:
- Basic AM modulation/demodulation system
- Advanced OFDM-based system with built-in synchronization
- Developed federated learning algorithms:
- Distributed model training
- Parameter aggregation via OTA computation
- Model convergence optimization
- Conducted comprehensive simulations:
- Channel estimation and compensation
- Synchronization performance analysis
- System throughput evaluation
Key Features
- Analog Over-the-Air computation
- Multi-device parameter aggregation
- OFDM-based communication
- Channel estimation and compensation
- Distributed model training
- Real-time parameter updates
Technical Stack
- MATLAB/Simulink
- Communication Systems
- Signal Processing
- Machine Learning
- OFDM Technology
- Channel Modeling
- System Optimization
Project Advisor
Prof. Howard Yang