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Found 8 papers, 1 papers with code
NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services
Towards personalized generative services, a collaborative cloud-edge methodology is promising, as it facilitates the effective orchestration of heterogeneous distributed communication and computing resources.
RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning
The rapid development of artificial intelligence (AI) over massive applications including Internet-of-things on cellular network raises the concern of technical challenges such as privacy, heterogeneity and resource efficiency.
FedNC: A Secure and Efficient Federated Learning Method with Network Coding
Federated Learning (FL) is a promising distributed learning mechanism which still faces two major challenges, namely privacy breaches and system efficiency.
FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learning
By adopting layer-wise pruning in local training and federated updating, we formulate an explicit FL pruning framework, FedLP (Federated Layer-wise Pruning), which is model-agnostic and universal for different types of deep learning models.
ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling
Then, we formulate the problem of selecting optimal IS weights and obtain the theoretical solutions.
How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning
In this paper, we develop a vertical-horizontal federated learning (VHFL) process, where the global feature is shared with the agents in a procedure similar to that of vertical FL without any extra communication rounds.
Semantic Communication with Adaptive Universal Transformer
With the development of deep learning (DL), natural language processing (NLP) makes it possible for us to analyze and understand a large amount of language texts.
Convergence Analysis and System Design for Federated Learning over Wireless Networks
Federated learning (FL) has recently emerged as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets.
