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Found 22 papers, 1 papers with code
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks
This paper aims to design an adaptive client sampling algorithm for FL over wireless networks that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time.
Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization
Toward the challenges, we propose an online problem approximation to reduce the problem complexity and optimize the resources to balance the needs of model training and inference.
Provably Convergent Federated Trilevel Learning
To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems.
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning
In this paper, we propose Federated knowledge distillation enabled by Adversarial Learning (FedAL) to address the data heterogeneity among clients.
Incentive Mechanism Design for Distributed Ensemble Learning
To this end, we propose an alternating algorithm that iteratively updates each learner's training data size and reward.
Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation
Incentive mechanism is crucial for federated learning (FL) when rational clients do not have the same interests in the global model as the server.
Optimization Design for Federated Learning in Heterogeneous 6G Networks
With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge.
Near Real-time CO$_2$ Emissions Based on Carbon Satellite and Artificial Intelligence
Here we provide the first method that combines the advanced artificial intelligence (AI) techniques and the carbon satellite monitor to quantify anthropogenic CO$_2$ emissions.
Insurance Contract for High Renewable Energy Integration
A proper insurance design needs to resolve the following two challenges: (i) users' reliability preference is private information; and (ii) the insurance design is tightly coupled with the renewable energy investment decision.
Cross-Silo Federated Learning: Challenges and Opportunities
Federated learning (FL) is an emerging technology that enables the training of machine learning models from multiple clients while keeping the data distributed and private.
Socially-Optimal Mechanism Design for Incentivized Online Learning
Multi-arm bandit (MAB) is a classic online learning framework that studies the sequential decision-making in an uncertain environment.
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling
This paper aims to design an adaptive client sampling algorithm that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time.
Time-of-use Pricing for Energy Storage Investment
Such a pricing scheme provides users with incentives to invest in behind-the-meter energy storage and to shift peak load towards low-price intervals.
Cost-Effective Federated Learning in Mobile Edge Networks
Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server without sharing their raw data.
Federated Few-Shot Learning with Adversarial Learning
In this paper, we propose a federated few-shot learning (FedFSL) framework to learn a few-shot classification model that can classify unseen data classes with only a few labeled samples.
Flatband-Induced Itinerant Ferromagnetism in RbCo$_2$Se$_2$
$A$Co$_2$Se$_2$ ($A$=K, Rb, Cs) is a homologue of the iron-based superconductor, $A$Fe$_2$Se$_2$.
Superconductivity Materials Science
Practical Speech Re-use Prevention in Voice-driven Services
We present AEOLUS, a security overlay that proactively embeds a dynamic acoustic nonce at the time of user interaction, and detects the presence of the embedded nonce in the recorded speech to ensure freshness.
Origin of the Electronic Structure in Single-Layer FeSe/SrTiO3 Films
These observations not only show the first direct evidence that the electronic structure of single-layer FeSe/SrTiO3 films originates from bulk FeSe through a combined effect of an electronic phase transition and an interfacial charge transfer, but also provide a quantitative basis for theoretical models in describing the electronic structure and understanding the superconducting mechanism in single-layer FeSe/SrTiO3 films.
Band Gap
Superconductivity
Strongly Correlated Electrons
Cost-Effective Federated Learning Design
In this paper, we analyze how to design adaptive FL that optimally chooses these essential control variables to minimize the total cost while ensuring convergence.
Contract-based Time-of-use Pricing for Energy Storage Investment
We also show that the proposed contracts can reduce the system social cost by over 30%, compared with no storage investment benchmark.
Systems and Control Systems and Control
Virtual Energy Storage Sharing and Capacity Allocation
In our simulation results, the proposed storage virtualization model can reduce the physical energy storage investment of the aggregator by 54. 3% and reduce the users' total costs by 34. 7%, compared to the case where users acquire their own physical storage.
Parametric Prediction from Parametric Agents
In order to elicit heterogeneous agents' private information and incentivize agents with different capabilities to act in the principal's best interest, we design an optimal joint incentive mechanism and prediction algorithm called COPE (COst and Prediction Elicitation), the analysis of which offers several valuable engineering insights.
