Decentralized federated learning with unreliable communications
Decentralized federated learning, inherited from decentralized learning, enables the edge
devices to collaborate on model training in a peer-to-peer manner without the assistance of …
devices to collaborate on model training in a peer-to-peer manner without the assistance of …
Lazily aggregated quantized gradient innovation for communication-efficient federated learning
This paper focuses on communication-efficient federated learning problem, and develops a
novel distributed quantized gradient approach, which is characterized by adaptive …
novel distributed quantized gradient approach, which is characterized by adaptive …
Design and analysis of uplink and downlink communications for federated learning
Communication has been known to be one of the primary bottlenecks of federated learning
(FL), and yet existing studies have not addressed the efficient communication design …
(FL), and yet existing studies have not addressed the efficient communication design …
Decentralized inference with graph neural networks in wireless communication systems
Graph neural network (GNN) is an efficient neural network model for graph data and is
widely used in different fields, including wireless communications. Different from other …
widely used in different fields, including wireless communications. Different from other …
Communication-efficient federated edge learning via optimal probabilistic device scheduling
Federated edge learning (FEEL) is a popular distributed learning framework that allows
privacy-preserving collaborative model training via periodic learning-updates …
privacy-preserving collaborative model training via periodic learning-updates …
Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems
The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …
solutions to replace the traditional model-driven approaches that proved to be not rich …
Wireless Federated Learning (WFL) for 6G Networks⁴Part I: Research Challenges and Future Trends
PS Bouzinis, PD Diamantoulakis… - IEEE …, 2021 - ieeexplore.ieee.org
Conventional machine learning techniques are conducted in a centralized manner.
Recently, the massive volume of generated wireless data, the privacy concerns and the …
Recently, the massive volume of generated wireless data, the privacy concerns and the …
Deploying federated learning in large-scale cellular networks: Spatial convergence analysis
The deployment of federated learning in a wireless network, called federated edge learning
(FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model …
(FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model …
Federated learning over multihop wireless networks with in-network aggregation
Communication limitation at the edge is widely recognized as a major bottleneck for
federated learning (FL). Multi-hop wireless networking provides a cost-effective solution to …
federated learning (FL). Multi-hop wireless networking provides a cost-effective solution to …
Machine learning for large-scale optimization in 6g wireless networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …