576 research outputs found
Exploiting Synthetic Data for Data Imbalance Problems: Baselines from a Data Perspective
We live in a vast ocean of data, and deep neural networks are no exception to
this. However, this data exhibits an inherent phenomenon of imbalance. This
imbalance poses a risk of deep neural networks producing biased predictions,
leading to potentially severe ethical and social consequences. To address these
challenges, we believe that the use of generative models is a promising
approach for comprehending tasks, given the remarkable advancements
demonstrated by recent diffusion models in generating high-quality images. In
this work, we propose a simple yet effective baseline, SYNAuG, that utilizes
synthetic data as a preliminary step before employing task-specific algorithms
to address data imbalance problems. This straightforward approach yields
impressive performance on datasets such as CIFAR100-LT, ImageNet100-LT,
UTKFace, and Waterbird, surpassing the performance of existing task-specific
methods. While we do not claim that our approach serves as a complete solution
to the problem of data imbalance, we argue that supplementing the existing data
with synthetic data proves to be an effective and crucial preliminary step in
addressing data imbalance concerns
Challenges on the way of implementing TCP over 5G networks
5G cellular communication, especially with its hugely available bandwidth provided by millimeter-wave, is a promising technology to fulfill the coming high demand for vast data rates. These networks can support new use cases such as Vehicle to Vehicle and augmented reality due to its novel features such as network slicing along with the mmWave multi-gigabit-per-second data rate. Nevertheless, 5G cellular networks suffer from some shortcomings, especially in high frequencies because of the intermittent nature of channels when the frequency rises. Non-line of sight state, is one of the significant issues that the new generation encounters. This drawback is because of the intense susceptibility of higher frequencies to blockage caused by obstacles and misalignment. This unique characteristic can impair the performance of the reliable transport layer widely deployed protocol, TCP, in attaining high throughput and low latency throughout a fair network. As a result, the protocol needs to adjust the congestion window size based on the current situation of the network. However, TCP is not able to adjust its congestion window efficiently, and it leads to throughput degradation of the protocol. This paper presents a comprehensive analysis of reliable end-to-end communications in 5G networks. It provides the analysis of the effects of TCP in 5G mmWave networks, the discussion of TCP mechanisms and parameters involved in the performance over 5G networks, and a survey of current challenges, solutions, and proposals. Finally, a feasibility analysis proposal of machine learning-based approaches to improve reliable end-to-end communications in 5G networks is presented.This work was supported by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de
Catalunya under Grant 2017 SGR 376.Peer ReviewedPostprint (published version
No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation
Ensuring fairness in Recommendation Systems (RSs) across demographic groups
is critical due to the increased integration of RSs in applications such as
personalized healthcare, finance, and e-commerce. Graph-based RSs play a
crucial role in capturing intricate higher-order interactions among entities.
However, integrating these graph models into the Federated Learning (FL)
paradigm with fairness constraints poses formidable challenges as this requires
access to the entire interaction graph and sensitive user information (such as
gender, age, etc.) at the central server. This paper addresses the pervasive
issue of inherent bias within RSs for different demographic groups without
compromising the privacy of sensitive user attributes in FL environment with
the graph-based model. To address the group bias, we propose F2PGNN (Fair
Federated Personalized Graph Neural Network), a novel framework that leverages
the power of Personalized Graph Neural Network (GNN) coupled with fairness
considerations. Additionally, we use differential privacy techniques to fortify
privacy protection. Experimental evaluation on three publicly available
datasets showcases the efficacy of F2PGNN in mitigating group unfairness by 47%
- 99% compared to the state-of-the-art while preserving privacy and maintaining
the utility. The results validate the significance of our framework in
achieving equitable and personalized recommendations using GNN within the FL
landscape.Comment: To appear as a full paper in AAAI 202
Focal-PETR: Embracing Foreground for Efficient Multi-Camera 3D Object Detection
The dominant multi-camera 3D detection paradigm is based on explicit 3D
feature construction, which requires complicated indexing of local image-view
features via 3D-to-2D projection. Other methods implicitly introduce geometric
positional encoding and perform global attention (e.g., PETR) to build the
relationship between image tokens and 3D objects. The 3D-to-2D perspective
inconsistency and global attention lead to a weak correlation between
foreground tokens and queries, resulting in slow convergence. We propose
Focal-PETR with instance-guided supervision and spatial alignment module to
adaptively focus object queries on discriminative foreground regions.
Focal-PETR additionally introduces a down-sampling strategy to reduce the
consumption of global attention. Due to the highly parallelized implementation
and down-sampling strategy, our model, without depth supervision, achieves
leading performance on the large-scale nuScenes benchmark and a superior speed
of 30 FPS on a single RTX3090 GPU. Extensive experiments show that our method
outperforms PETR while consuming 3x fewer training hours. The code will be made
publicly available.Comment: Tech Repor
Improving Inter-service bandwidth fairness in Wireless Mesh Networks
Includes bibliographical references.We are currently experiencing many technological advances and as a result, a lot of applications and services are developed for use in homes, offices and out in the field. In order to attract users and customers, most applications and / or services are loaded with graphics, pictures and movie clips. This unfortunately means most of these next generation services put a lot of strain on networking resources, namely bandwidth. Efficient management of bandwidth in next generation wireless network is therefore important for ensuring fairness in bandwidth allocation amongst multiple services with diverse quality of service needs. A number of algorithms have been proposed for fairness in bandwidth allocation in wireless networks, and some researchers have used game theory to model the different aspects of fairness. However, most of the existing algorithms only ensure fairness for individual requests and disregard fairness among the classes of services while some other algorithms ensure fairness for the classes of services and disregard fairness among individual requests
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
Artificial intelligence (AI) continues to transform data analysis in many
domains. Progress in each domain is driven by a growing body of annotated data,
increased computational resources, and technological innovations. In medicine,
the sensitivity of the data, the complexity of the tasks, the potentially high
stakes, and a requirement of accountability give rise to a particular set of
challenges. In this review, we focus on three key methodological approaches
that address some of the particular challenges in AI-driven medical decision
making. (1) Explainable AI aims to produce a human-interpretable justification
for each output. Such models increase confidence if the results appear
plausible and match the clinicians expectations. However, the absence of a
plausible explanation does not imply an inaccurate model. Especially in highly
non-linear, complex models that are tuned to maximize accuracy, such
interpretable representations only reflect a small portion of the
justification. (2) Domain adaptation and transfer learning enable AI models to
be trained and applied across multiple domains. For example, a classification
task based on images acquired on different acquisition hardware. (3) Federated
learning enables learning large-scale models without exposing sensitive
personal health information. Unlike centralized AI learning, where the
centralized learning machine has access to the entire training data, the
federated learning process iteratively updates models across multiple sites by
exchanging only parameter updates, not personal health data. This narrative
review covers the basic concepts, highlights relevant corner-stone and
state-of-the-art research in the field, and discusses perspectives.Comment: This paper is accepted in IEEE CAA Journal of Automatica Sinica, Nov.
10 202
Trustworthy Federated Learning: A Survey
Federated Learning (FL) has emerged as a significant advancement in the field
of Artificial Intelligence (AI), enabling collaborative model training across
distributed devices while maintaining data privacy. As the importance of FL
increases, addressing trustworthiness issues in its various aspects becomes
crucial. In this survey, we provide an extensive overview of the current state
of Trustworthy FL, exploring existing solutions and well-defined pillars
relevant to Trustworthy . Despite the growth in literature on trustworthy
centralized Machine Learning (ML)/Deep Learning (DL), further efforts are
necessary to identify trustworthiness pillars and evaluation metrics specific
to FL models, as well as to develop solutions for computing trustworthiness
levels. We propose a taxonomy that encompasses three main pillars:
Interpretability, Fairness, and Security & Privacy. Each pillar represents a
dimension of trust, further broken down into different notions. Our survey
covers trustworthiness challenges at every level in FL settings. We present a
comprehensive architecture of Trustworthy FL, addressing the fundamental
principles underlying the concept, and offer an in-depth analysis of trust
assessment mechanisms. In conclusion, we identify key research challenges
related to every aspect of Trustworthy FL and suggest future research
directions. This comprehensive survey serves as a valuable resource for
researchers and practitioners working on the development and implementation of
Trustworthy FL systems, contributing to a more secure and reliable AI
landscape.Comment: 45 Pages, 8 Figures, 9 Table
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