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AssigneeOdinelabs Inc
InventorsUmuralp KAYTAZ, Ian Fuat Akyildiz, Bulent Kaytaz
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Example implementations encompass systems and methods for federated learning-assisted multi-task semantic communications tailored for 5G/6G wireless systems. A Centralized Server (CS) hosts a knowledge base with interconnected Machine Learning (ML)-based semantic codec models, extracting and synthesizing semantic information across various tasks using single or multi-modal observations. The CS connects with Decentralized Servers (DSs) and Edge Servers (ESs), which handle task-related data transmission and reception. This setup enables training of global models for semantic communications through federated learning. ML models in DS knowledge bases allow local model updates, leveraging ES-assisted transmissions via a Local Model Aggregator (LMA). The CS aggregates model weights from the DSs using a Global Model Aggregator (GMA). Semantic transmission requests can be initiated from ESs and forwarded to the CS, which responds by providing semantic encoding and decoding models to ensure efficient communication of semantic information between involved parties.
A system of Multi-Task Semantic Communications (MTSC) via Federated Learning (FL), comprising:
A method of federated learning (FL) for Multi-Task Semantic Communication (MTSC), comprising:
A method of multi-task semantic communication using a single Semantic Information Source (SIS), comprising:
A method of locally coordinated federated learning (FL) using multiple distributed servers (DSs), comprising:
Dependencies are inferred from the claim text.