FedMLC: White-box Model Watermarking for Copyright Protection in Federated Learning for IoT Environment
HeLoRA: LoRA-heterogeneous Federated Fine-tuning for Foundation Models
Foundation models (FMs) have achieved state-of-the-art performance across various domains, benefiting from their vast number of parameters and the extensive amount of […]
A Web-Based Solution for Federated Learning With LLM-Based Automation
Federated Learning (FL) offers a promising approach for collaborative machine learning across distributed devices. However, its adoption is hindered by the complexity […]
Federated Learning based Base Station Selection using LiDAR
Base station (BS) selection is important in establishing reliable communication links in millimeter-wave (mmWave) systems. The selection procedure typically requires each BS […]
Scalable and Resource-Efficient Second-Order Federated Learning via Over-the-Air Aggregation
Second-order federated learning (FL) algorithms offer faster convergence than their first-order counterparts by leveraging curvature information. However, they are hindered by high […]
PipeSFL: A Fine-Grained Parallelization Framework for Split Federated Learning on Heterogeneous Clients
Split Federated Learning (SFL) improves scalability of Split Learning (SL) by enabling parallel computing of the learning tasks on multiple clients. However, […]
Communication-Efficient Federated Deep Reinforcement Learning based Cooperative Edge Caching in Fog Radio Access Networks
In this paper, the cooperative edge caching problem is studied in fog radio access networks (F-RANs). Given the non-deterministic polynomial hard (NP-hard) […]
Quantized FedPD (QFedPD): Beyond Conventional Wisdom – The Energy Benefits of Frequent Communication
Federated Averaging (FedAvg) is a well-recognized framework for distributed learning that efficiently manages communication. Several algorithms have emerged to enhance the communication […]
Federated Learning and Meta Learning: Approaches, Applications, and Directions
Over the past few years, significant advancements have been made in the field of machine learning (ML) to address resource management, interference […]
Energy-Aware Federated Learning With Distributed User Sampling and Multichannel ALOHA
Distributed learning on edge devices has attracted increased attention with the advent of federated learning (FL). Notably, edge devices often have limited […]