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Found 7 papers, 1 papers with code
Advances and Open Challenges in Federated Learning with Foundation Models
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while addressing concerns of privacy, data decentralization, and computational efficiency.
Intelligent Agents for Auction-based Federated Learning: A Survey
To enhance the efficiency in AFL decision support for stakeholders (i. e., data consumers, data owners, and the auctioneer), intelligent agent-based techniques have emerged.
Multi-Session Budget Optimization for Forward Auction-based Federated Learning
Based on hierarchical reinforcement learning, MultiBOS-AFL jointly optimizes inter-session budget pacing and intra-session bidding for AFL MUs, with the objective of maximizing the total utility.
Hierarchical Federated Learning Incentivization for Gas Usage Estimation
Federated learning (FL) offers a solution to this problem by enabling local data processing on each participant, such as gas companies and heating stations.
Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning
However, this assumption is not realistic in practical AFL marketplaces in which multiple data consumers can compete to attract data owners to join their respective FL tasks.
Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioning
As such, building trustworthy AIRTB auctioning systems has emerged as an important direction of research in this field in recent years.
MuFA (Multi-type Fourier Analyzer): A tool for batch generation of MuMax3 input scripts and multi-type Fourier analysis from micromagnetic simulation output data
We present a tool for batch generation of input scripts and multi-type Fourier analysis from simulation results for the micromagnetic software MuMax3.
Signal Processing
