Search Results for author: Ali Anwar

Found 28 papers, 6 papers with code

Privacy-Aware Semantic Cache for Large Language Models

no code implementations5 Mar 2024 Waris Gill, Mohamed Elidrisi, Pallavi Kalapatapu, 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 cache false hit rates.

Federated Learning

ProvFL: Client-Driven Interpretability of Global Model Predictions in Federated Learning

no code implementations21 Dec 2023 Waris Gill, Ali Anwar, Muhammad Ali Gulzar

Regardless of the quality of the global model or if it has a fault, understanding the model's origin is equally important for debugging, interpretability, and explainability in federated learning.

Explainable Models Fault localization +3

SeaDSC: A video-based unsupervised method for dynamic scene change detection in unmanned surface vehicles

no code implementations20 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.

Change Detection Motion Planning +5

Autonomous Port Navigation With Ranging Sensors Using Model-Based Reinforcement Learning

no code implementations17 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.

Model-based Reinforcement Learning Navigate +1

Safety Aware Autonomous Path Planning Using Model Predictive Reinforcement Learning for Inland Waterways

no code implementations16 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.

Navigate reinforcement-learning

FedDefender: Backdoor Attack Defense in Federated Learning

1 code implementation2 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.

Backdoor Attack Data Poisoning +4

A Framework for Incentivized Collaborative Learning

no code implementations26 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.

Federated Learning

Attention Based Feature Fusion For Multi-Agent Collaborative Perception

no code implementations3 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).

Graph Attention Object +2

FedDebug: Systematic Debugging for Federated Learning Applications

1 code implementation9 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.

Fault localization Federated Learning

Towards cost-effective and resource-aware aggregation at Edge for Federated Learning

no code implementations16 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.

Federated Learning

LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning

no code implementations26 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.

Federated Learning

FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data

no code implementations5 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.

Federated Learning Privacy Preserving

Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning

no code implementations1 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.

Federated Learning

Wukong: A Scalable and Locality-Enhanced Framework for Serverless Parallel Computing

4 code implementations14 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

FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers

no code implementations12 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.

Federated Learning

Learning to Communicate Using Counterfactual Reasoning

no code implementations12 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.

counterfactual Counterfactual Reasoning +2

TiFL: A Tier-based Federated Learning System

no code implementations25 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.

Federated Learning

HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning

no code implementations12 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.

Federated Learning Privacy Preserving

Towards Federated Graph Learning for Collaborative Financial Crimes Detection

no code implementations19 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.

Federated Learning Graph Learning

Characterizing Co-located Datacenter Workloads: An Alibaba Case Study

1 code implementation8 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

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