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Found 20 papers, 3 papers with code
Coexistence of Pull and Push Communication in Wireless Access for IoT Devices
We consider a setup with Internet of Things (IoT), where a base station (BS) collects data from nodes that use two different communication modes.
Greedy Shapley Client Selection for Communication-Efficient Federated Learning
The standard client selection algorithms for Federated Learning (FL) are often unbiased and involve uniform random sampling of clients.
Privacy-Aware Data Acquisition under Data Similarity in Regression Markets
Data markets facilitate decentralized data exchange for applications such as prediction, learning, or inference.
Value-Based Reinforcement Learning for Digital Twins in Cloud Computing
The setup considered in the paper consists of sensors in a Networked Control System that are used to build a digital twin (DT) model of the system dynamics.
ARQ for Active Learning at the Edge
Conventional retransmission (ARQ) protocols are designed with the goal of ensuring the correct reception of all the individual transmitter's packets at the receiver.
Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participation
A client has to decide its transmission plan on when not to participate in FL.
On-board Change Detection for Resource-efficient Earth Observation with LEO Satellites
The amount of data generated by Earth observation satellites can be enormous, which poses a great challenge to the satellite-to-ground connections with limited rate.
The Role of Game Networking in the Fusion of Physical and Digital Worlds through 6G Wireless
From the viewpoint of communication and networking, this will represent an evolution of the game networking technology, designed to interconnect massive users in real-time online gaming environments.
Scheduling Policy for Value-of-Information (VoI) in Trajectory Estimation for Digital Twins
This paper presents an approach to schedule observations from different sensors in an environment to ensure their timely delivery and build a digital twin (DT) model of the system dynamics.
FedToken: Tokenized Incentives for Data Contribution in Federated Learning
Incentives that compensate for the involved costs in the decentralized training of a Federated Learning (FL) model act as a key stimulus for clients' long-term participation.
Strategic Coalition for Data Pricing in IoT Data Markets
Finally, we reveal the structure of the formulated problem as a distributed coalition game and solve it following the simplified split-and-merge algorithm.
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning
Therefore, to develop robust generalized global and personalized models, conventional FL methods need redesigning the knowledge aggregation from biased local models while considering huge divergence of learning parameters due to skewed client data.
A Contribution-based Device Selection Scheme in Federated Learning
In a Federated Learning (FL) setup, a number of devices contribute to the training of a common model.
A Marketplace for Trading AI Models based on Blockchain and Incentives for IoT Data
An emerging paradigm in ML is a federated approach where the learning model is delivered to a group of heterogeneous agents partially, allowing agents to train the model locally with their own data.
Edge-assisted Democratized Learning Towards Federated Analytics
Therefore, we need to design a robust learning mechanism than the FL that (i) unleashes a viable infrastructure for FA and (ii) trains learning models with better generalization capability.
An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach
In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users.
Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems
Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper.
Distributed and Democratized Learning: Philosophy and Research Challenges
Consequently, many emerging cross-device AI applications will require a transition from traditional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform multiple complex learning tasks.
Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
IoT devices with intelligence require the use of effective machine learning paradigms.
Distributed, Parallel, and Cluster Computing
A Crowdsourcing Framework for On-Device Federated Learning
Federated learning (FL) rests on the notion of training a global model in a decentralized manner.
