no code implementations • 2 Dec 2024 • Linh Trinh, Siegfried Mercelis, Ali Anwar
We elaborate several challenges and potential opportunities for research and development in USV vision based on a thorough analysis of current datasets and deep learning techniques.
no code implementations • 7 Nov 2024 • Bavo Lesy, Ali Anwar, Siegfried Mercelis
Recently, there has been growing interest in autonomous shipping due to its potential to improve maritime efficiency and safety.
no code implementations • 24 Oct 2024 • Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao, Yi Zhou, Nathalie Baracaldo, Jie Ding, Ali Anwar
Ensuring that generative AI systems align with human values is essential but challenging, especially when considering multiple human values and their potential trade-offs.
no code implementations • 10 Sep 2024 • Azal Ahmad Khan, Ahmad Faraz Khan, Haider Ali, Ali Anwar
Personalized Federated Learning (pFL) holds immense promise for tailoring machine learning models to individual users while preserving data privacy.
1 code implementation • 8 Sep 2024 • Qi Le, Enmao Diao, Xinran Wang, Vahid Tarokh, Jie Ding, Ali Anwar
Federated Learning (FL) is a collaborative machine learning framework that allows multiple users to train models utilizing their local data in a distributed manner.
no code implementations • 19 Jul 2024 • Linh Trinh, Ali Anwar, Siegfried Mercelis
In this study, we focus our attention on improving the classification performance while keeping the computational cost of our solution low.
1 code implementation • 16 Jul 2024 • Linh Trinh, Ali Anwar, Siegfried Mercelis
In this paper, we present a data selection method that is practical, flexible, and efficient for assessment of autonomous vehicles.
no code implementations • 14 Jul 2024 • Linh Trinh, Ali Anwar, Siegfried Mercelis
In our method, we propose a scheme for dealing with the invariance of multiple data sources while training a model on multiple data sources.
1 code implementation • 14 Jun 2024 • Siemen Herremans, Ali Anwar, Siegfried Mercelis
By learning a pessimistic world model and demonstrating its role in improving policy robustness, our research contributes towards making (model-based) RL more robust.
1 code implementation • 22 Apr 2024 • Enmao Diao, Qi Le, Suya Wu, Xinran Wang, Ali Anwar, Jie Ding, Vahid Tarokh
We introduce Collaborative Adaptation (ColA) with Gradient Learning (GL), a parameter-free, model-agnostic fine-tuning approach that decouples the computation of the gradient of hidden representations and parameters.
no code implementations • 5 Mar 2024 • Waris Gill, Mohamed Elidrisi, Pallavi Kalapatapu, Ammar Ahmed, Ali Anwar, Muhammad Ali Gulzar
By placing a local cache in each user's device and using FL, MeanCache reduces the latency and costs and enhances model performance, resulting in lower false hit rates.
2 code implementations • 21 Dec 2023 • Waris Gill, Ali Anwar, Muhammad Ali Gulzar
We introduce TraceFL, a fine-grained neuron provenance capturing mechanism that identifies clients responsible for a global model's prediction by tracking the flow of information from individual clients to the global model.
no code implementations • 20 Nov 2023 • Linh Trinh, Ali Anwar, Siegfried Mercelis
To the best of our understanding, this work represents the first investigation of scene change detection in the maritime vision application.
no code implementations • 17 Nov 2023 • Siemen Herremans, Ali Anwar, Arne Troch, Ian Ravijts, Maarten Vangeneugden, Siegfried Mercelis, Peter Hellinckx
The proposed methodology is based on a machine learning approach that has recently set benchmark results in various domains: model-based reinforcement learning.
no code implementations • 16 Nov 2023 • Astrid Vanneste, Simon Vanneste, Olivier Vasseur, Robin Janssens, Mattias Billast, Ali Anwar, Kevin Mets, Tom De Schepper, Siegfried Mercelis, Peter Hellinckx
We demonstrate our approach on two scenarios and compare the resulting path with path planning using a Frenet frame and path planning based on a proximal policy optimization (PPO) agent.
1 code implementation • 2 Jul 2023 • Waris Gill, Ali Anwar, Muhammad Ali Gulzar
Federated Learning (FL) is a privacy-preserving distributed machine learning technique that enables individual clients (e. g., user participants, edge devices, or organizations) to train a model on their local data in a secure environment and then share the trained model with an aggregator to build a global model collaboratively.
no code implementations • 26 May 2023 • Xinran Wang, Qi Le, Ahmad Faraz Khan, Jie Ding, Ali Anwar
Collaborations among various entities, such as companies, research labs, AI agents, and edge devices, have become increasingly crucial for achieving machine learning tasks that cannot be accomplished by a single entity alone.
no code implementations • 3 May 2023 • Ahmed N. Ahmed, Siegfried Mercelis, Ali Anwar
To improve the precision of object detection and alleviate limited network resources, we propose an intermediate collaborative perception solution in the form of a graph attention network (GAT).
no code implementations • 15 Apr 2023 • Ahmad Faraz Khan, Xinran Wang, Qi Le, Zain ul Abdeen, Azal Ahmad Khan, Haider Ali, Ming Jin, Jie Ding, Ali R. Butt, Ali Anwar
Our approach enhances the personalized model appeal for self-aware clients with high-quality data leading to their active and consistent participation.
no code implementations • 14 Apr 2023 • Jaime Spencer, C. Stella Qian, Michaela Trescakova, Chris Russell, Simon Hadfield, Erich W. Graf, Wendy J. Adams, Andrew J. Schofield, James Elder, Richard Bowden, Ali Anwar, Hao Chen, Xiaozhi Chen, Kai Cheng, Yuchao Dai, Huynh Thai Hoa, Sadat Hossain, Jianmian Huang, Mohan Jing, Bo Li, Chao Li, Baojun Li, Zhiwen Liu, Stefano Mattoccia, Siegfried Mercelis, Myungwoo Nam, Matteo Poggi, Xiaohua Qi, Jiahui Ren, Yang Tang, Fabio Tosi, Linh Trinh, S. M. Nadim Uddin, Khan Muhammad Umair, Kaixuan Wang, YuFei Wang, Yixing Wang, Mochu Xiang, Guangkai Xu, Wei Yin, Jun Yu, Qi Zhang, Chaoqiang Zhao
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC).
1 code implementation • 9 Jan 2023 • Waris Gill, Ali Anwar, Muhammad Ali Gulzar
FedDebug's interactive debugging incurs 1. 2% overhead during training, while it localizes a faulty client in only 2. 1% of a round's training time.
no code implementations • 17 Dec 2022 • Qi Le, Enmao Diao, Xinran Wang, Ali Anwar, Vahid Tarokh, Jie Ding
Recommender Systems (RSs) have become increasingly important in many application domains, such as digital marketing.
no code implementations • 16 Apr 2022 • Ahmad Faraz Khan, Yuze Li, Xinran Wang, Sabaat Haroon, Haider Ali, Yue Cheng, Ali R. Butt, Ali Anwar
Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source.
no code implementations • 25 Feb 2022 • Nathalie Baracaldo, Ali Anwar, Mark Purcell, Ambrish Rawat, Mathieu Sinn, Bashar Altakrouri, Dian Balta, Mahdi Sellami, Peter Kuhn, Ulrich Schopp, Matthias Buchinger
Federated Learning (FL) is a novel paradigm for the shared training of models based on decentralized and private data.
1 code implementation • 29 Nov 2021 • Sixing Yu, Phuong Nguyen, Waqwoya Abebe, Wei Qian, Ali Anwar, Ali Jannesari
Federated learning~(FL) facilitates the training and deploying AI models on edge devices.
no code implementations • 26 Jul 2021 • Kamala Varma, Yi Zhou, Nathalie Baracaldo, Ali Anwar
This global model can be corrupted when Byzantine workers send malicious gradients, which necessitates robust methods for aggregating gradients that mitigate the adverse effects of Byzantine inputs.
no code implementations • 13 Jun 2021 • Sixing Yu, Phuong Nguyen, Ali Anwar, Ali Jannesari
Our approach reduces up to 50\% FLOPs inference of DNNs on edge devices while maintaining the model's quality.
no code implementations • 5 Mar 2021 • Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, James Joshi, Heiko Ludwig
We empirically demonstrate the applicability for multiple types of ML models and show a reduction of 10%-70% of training time and 80% to 90% in data transfer with respect to the state-of-the-art approaches.
no code implementations • 1 Feb 2021 • Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, Feng Yan
Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks.
4 code implementations • 14 Oct 2020 • Benjamin Carver, Jingyuan Zhang, Ao Wang, Ali Anwar, Panruo Wu, Yue Cheng
Serverless computing is increasingly being used for parallel computing, which have traditionally been implemented as stateful applications.
Distributed, Parallel, and Cluster Computing
no code implementations • 12 Oct 2020 • Zheng Chai, Yujing Chen, Ali Anwar, Liang Zhao, Yue Cheng, Huzefa Rangwala
By bridging the synchronous and asynchronous training through tiering, FedAT minimizes the straggler effect with improved convergence speed and test accuracy.
1 code implementation • 22 Jul 2020 • Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, Mark Purcell, Ambrish Rawat, Tran Minh, Naoise Holohan, Supriyo Chakraborty, Shalisha Whitherspoon, Dean Steuer, Laura Wynter, Hifaz Hassan, Sean Laguna, Mikhail Yurochkin, Mayank Agarwal, Ebube Chuba, Annie Abay
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume.
no code implementations • 12 Jun 2020 • Simon Vanneste, Astrid Vanneste, Kevin Mets, Tom De Schepper, Ali Anwar, Siegfried Mercelis, Steven Latré, Peter Hellinckx
The credit assignment problem, the non-stationarity of the communication environment and the creation of influenceable agents are major challenges within this research field which need to be overcome in order to learn a valid communication protocol.
no code implementations • 25 Jan 2020 • Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, Yue Cheng
To this end, we propose TiFL, a Tier-based Federated Learning System, which divides clients into tiers based on their training performance and selects clients from the same tier in each training round to mitigate the straggler problem caused by heterogeneity in resource and data quantity.
no code implementations • 12 Dec 2019 • Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Heiko Ludwig
Participants in a federated learning process cooperatively train a model by exchanging model parameters instead of the actual training data, which they might want to keep private.
no code implementations • 19 Sep 2019 • Toyotaro Suzumura, Yi Zhou, Natahalie Baracaldo, Guangnan Ye, Keith Houck, Ryo Kawahara, Ali Anwar, Lucia Larise Stavarache, Yuji Watanabe, Pablo Loyola, Daniel Klyashtorny, Heiko Ludwig, Kumar Bhaskaran
Advances in technology used in this domain, including machine learning based approaches, can improve upon the effectiveness of financial institutions' existing processes, however, a key challenge that most financial institutions continue to face is that they address financial crimes in isolation without any insight from other firms.
1 code implementation • 7 Dec 2018 • Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, Yi Zhou
Federated learning facilitates the collaborative training of models without the sharing of raw data.
1 code implementation • 8 Aug 2018 • Yue Cheng, Zheng Chai, Ali Anwar
Warehouse-scale cloud datacenters co-locate workloads with different and often complementary characteristics for improved resource utilization.
Distributed, Parallel, and Cluster Computing