Search Results for author:
Found 25 papers, 8 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.
Predictive Handover Strategy in 6G and Beyond: A Deep and Transfer Learning Approach
Next-generation cellular networks will evolve into more complex and virtualized systems, employing machine learning for enhanced optimization and leveraging higher frequency bands and denser deployments to meet varied service demands.
From Similarity to Superiority: Channel Clustering for Time Series Forecasting
Motivated by our observation of a correlation between the time series model's performance boost against channel mixing and the intrinsic similarity on a pair of channels, we developed a novel and adaptable Channel Clustering Module (CCM).
Machine Learning on Blockchain Data: A Systematic Mapping Study
Conclusion: The results confirm that ML applied to blockchain data is a relevant and a growing topic of interest both in the literature and in practice.
An Item is Worth a Prompt: Versatile Image Editing with Disentangled Control
Building on the success of text-to-image diffusion models (DPMs), image editing is an important application to enable human interaction with AI-generated content.
Efficient High-Resolution Time Series Classification via Attention Kronecker Decomposition
The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains.
Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN
In software-defined networking (SDN), the implementation of distributed SDN controllers, with each controller responsible for managing a specific sub-network or domain, plays a critical role in achieving a balance between centralized control, scalability, reliability, and network efficiency.
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency
In this work, we develop an adaptive compression-aware split learning method ('deprune') to improve and train deep learning models so that they are much more network-efficient, which would make them ideal to deploy in weaker devices with the help of edge-cloud resources.
Mobility as a Resource (MaaR) for resilient human-centric automation: a vision paper
However, as observed in other fields (e. g. cloud computing resource management) we argue that mobility will evolve from a service to a resource (i. e., Mobility as a Resource, MaaR).
Leveraging Machine Learning for Multichain DeFi Fraud Detection
Since the inception of permissionless blockchains with Bitcoin in 2008, it became apparent that their most well-suited use case is related to making the financial system and its advantages available to everyone seamlessly without depending on any trusted intermediaries.
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.
Monetary Policy, Digital Assets, and DeFi Activity
We also show that some borrowing interest rates in the Ethereum DeFi ecosystem are affected positively by unexpected changes in monetary policy.
Robust and Resource-efficient Machine Learning Aided Viewport Prediction in Virtual Reality
In this work, we propose a novel meta learning based viewport prediction paradigm to alleviate the worst prediction performance and ensure the robustness of viewport prediction.
Deep Reinforcement Learning-based Rebalancing Policies for Profit Maximization of Relay Nodes in Payment Channel Networks
Payment channel networks (PCNs) are a layer-2 blockchain scalability solution, with its main entity, the payment channel, enabling transactions between pairs of nodes "off-chain," thus reducing the burden on the layer-1 network.
An Overview of the Data-Loader Landscape: Comparative Performance Analysis
Dataloaders, in charge of moving data from storage into GPUs while training machine learning models, might hold the key to drastically improving the performance of training jobs.
Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting
Traffic flow forecasting on graphs has real-world applications in many fields, such as transportation system and computer networks.
Debt-Financed Collateral and Stability Risks in the DeFi Ecosystem
The rise of Decentralized Finance ("DeFi") on the Ethereum blockchain has enabled the creation of lending platforms, which serve as marketplaces to lend and borrow digital currencies.
KerGNNs: Interpretable Graph Neural Networks with Graph Kernels
We also show that the trained graph filters in KerGNNs can reveal the local graph structures of the dataset, which significantly improves the model interpretability compared with conventional GNN models.
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.
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 Learning with Spiking Neural Networks
To validate the proposed federated learning framework, we experimentally evaluate the advantages of SNNs on various aspects of federated learning with CIFAR10 and CIFAR100 benchmarks.
State-Dependent Processing in Payment Channel Networks for Throughput Optimization
Payment channel networks (PCNs) have emerged as a scalability solution for blockchains built on the concept of a payment channel: a setting that allows two nodes to safely transact between themselves in high frequencies based on pre-committed peer-to-peer balances.
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.
Optimal Bidding Strategy for Maker Auctions
The Maker Protocol is a decentralized finance application that enables collateralized lending.
Model Pruning Enables Efficient Federated Learning on Edge Devices
To overcome this challenge, we propose PruneFL -- a novel FL approach with adaptive and distributed parameter pruning, which adapts the model size during FL to reduce both communication and computation overhead and minimize the overall training time, while maintaining a similar accuracy as the original model.
