Search Results for author:
Found 20 papers, 2 papers with code
Adaptive Federated Learning Over the Air
In contrast, an Adam-like algorithm converges at the $\mathcal{O}( 1/T )$ rate, demonstrating its advantage in expediting the model training process.
Version age-based client scheduling policy for federated learning
Motivated by this, we introduce the novel concept of Version Age of Information (VAoI) to FL.
Spectral Co-Distillation for Personalized Federated Learning
Personalized federated learning (PFL) has been widely investigated to address the challenge of data heterogeneity, especially when a single generic model is inadequate in satisfying the diverse performance requirements of local clients simultaneously.
Value of Information and Timing-aware Scheduling for Federated Learning
Federated Learning (FL) offers a solution by preserving data privacy during training.
Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration
Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on task-oriented connections, especially in the context of the thriving development of pre-trained foundation models (PFM) and the evolving vision of 6G native artificial intelligence (AI).
The Role of Federated Learning in a Wireless World with Foundation Models
Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications.
Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing
This work proposes a novel solution to address these challenges, namely personalized federated deep reinforcement learning (PF-DRL), for multi-UAV trajectory optimization.
Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning
Federated edge learning is envisioned as the bedrock of enabling intelligence in next-generation wireless networks, but the limited spectral resources often constrain its scalability.
DPP-based Client Selection for Federated Learning with Non-IID Data
This paper proposes a client selection (CS) method to tackle the communication bottleneck of federated learning (FL) while concurrently coping with FL's data heterogeneity issue.
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks
Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks.
Personalizing Federated Learning with Over-the-Air Computations
Additionally, we leverage a bi-level optimization framework to personalize the federated learning model so as to cope with the data heterogeneity issue.
Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks
Experimental results verify the effectiveness of PerFedS2 in saving training time as well as guaranteeing the convergence of training loss, in contrast to synchronous and asynchronous PFL algorithms.
Towards Federated Long-Tailed Learning
Data privacy and class imbalance are the norm rather than the exception in many machine learning tasks.
Federated Stochastic Gradient Descent Begets Self-Induced Momentum
Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistical model utilizing the data and computation resources of the clients without directly exposing their privacy-sensitive data.
Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks
Finally, we provide experiments to evaluate the learning performance of HFL and our MACFL.
Optimizing the Long-Term Average Reward for Continuing MDPs: A Technical Report
cost), by integrating R-learning, a tabular reinforcement learning (RL) algorithm tailored for maximizing the long-term average reward, and traditional DRL algorithms, initially developed to optimize the discounted long-term cumulative reward rather than the average one.
Multi-Armed Bandit Based Client Scheduling for Federated Learning
In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels.
Federated Learning with Differential Privacy: Algorithms and Performance Analysis
Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i. e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level.
Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks
Federated learning (FL) is a machine learning model that preserves data privacy in the training process.
Information Theory Signal Processing Information Theory
Scheduling Policies for Federated Learning in Wireless Networks
Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration.
Information Theory Signal Processing Information Theory
