Search Results for author: Carlee Joe-Wong

Found 33 papers, 12 papers with code

Fair Concurrent Training of Multiple Models in Federated Learning

no code implementations22 Apr 2024 Marie Siew, Haoran Zhang, Jong-Ik Park, Yuezhou Liu, Yichen Ruan, Lili Su, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong

We show how our fairness-based learning and incentive mechanisms impact training convergence and finally evaluate our algorithm with multiple sets of learning tasks on real world datasets.

Fairness Federated Learning

TREACLE: Thrifty Reasoning via Context-Aware LLM and Prompt Selection

no code implementations17 Apr 2024 Xuechen Zhang, Zijian Huang, Ege Onur Taga, Carlee Joe-Wong, Samet Oymak, Jiasi Chen

Recent successes in natural language processing have led to the proliferation of large language models (LLMs) by multiple providers.

GSM8K Navigate

Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics

no code implementations15 Apr 2024 Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong, Lili Su

It consists of a parameter server and a possibly large collection of clients (e. g., in cross-device federated learning) that may operate in congested and changing environments.

Federated Learning

CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT

no code implementations27 Mar 2024 Yi Hu, Jinhang Zuo, Alanis Zhao, Bob Iannucci, Carlee Joe-Wong

Foundation models (FMs) emerge as a promising solution to harness distributed and diverse environmental data by leveraging prior knowledge to understand the complicated temporal and spatial correlations within heterogeneous datasets.

Federated Learning Representation Learning

An LLM-Based Digital Twin for Optimizing Human-in-the Loop Systems

1 code implementation25 Mar 2024 Hanqing Yang, Marie Siew, Carlee Joe-Wong

In this paper, we present a case study that employs LLM agents to mimic the behaviors and thermal preferences of various population groups (e. g. young families, the elderly) in a shopping mall.

DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset

no code implementations20 Oct 2023 Weijie Liu, Xiaoxi Zhang, Jingpu Duan, Carlee Joe-Wong, Zhi Zhou, Xu Chen

Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data.

Federated Learning Navigate

Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning

no code implementations17 Oct 2023 Taejin Kim, Jiarui Li, Shubhranshu Singh, Nikhil Madaan, Carlee Joe-Wong

Our research, initially spurred by test-time evasion attacks, investigates the intersection of adversarial training and backdoor attacks within federated learning, introducing Adversarial Robustness Unhardening (ARU).

Adversarial Robustness Federated Learning

Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learning

no code implementations19 Aug 2023 Yi Hu, Jinhang Zuo, Bob Iannucci, Carlee Joe-Wong

Internet of Things (IoT) technologies have enabled numerous data-driven mobile applications and have the potential to significantly improve environmental monitoring and hazard warnings through the deployment of a network of IoT sensors.

Intelligent Communication Multi-agent Reinforcement Learning +1

RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement Learning

1 code implementation7 Aug 2023 Jingdi Chen, Tian Lan, Carlee Joe-Wong

This result enables us to recast multi-agent communication into a novel online clustering problem over the local observations at each agent, with messages as cluster labels and the upper bound on the return gap as clustering loss.

Clustering Multi-agent Reinforcement Learning +2

Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications

no code implementations1 Jun 2023 Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong, Lili Su

Specifically, in each round $t$, the link between the PS and client $i$ is active with probability $p_i^t$, which is $\textit{unknown}$ to both the PS and the clients.

Federated Learning

GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing

1 code implementation23 May 2023 Yi Hu, Chaoran Zhang, Edward Andert, Harshul Singh, Aviral Shrivastava, James Laudon, Yanqi Zhou, Bob Iannucci, Carlee Joe-Wong

Careful placement of a computational application within a target device cluster is critical for achieving low application completion time.


FedRule: Federated Rule Recommendation System with Graph Neural Networks

2 code implementations13 Nov 2022 Yuhang Yao, Mohammad Mahdi Kamani, Zhongwei Cheng, Lin Chen, Carlee Joe-Wong, Tianqiang Liu

Much of the value that IoT (Internet-of-Things) devices bring to ``smart'' homes lies in their ability to automatically trigger other devices' actions: for example, a smart camera triggering a smart lock to unlock a door.

Link Prediction Recommendation Systems

Characterizing Internal Evasion Attacks in Federated Learning

1 code implementation17 Sep 2022 Taejin Kim, Shubhranshu Singh, Nikhil Madaan, Carlee Joe-Wong

However, combining adversarial training with personalized federated learning frameworks increases relative internal attack robustness by 60% compared to federated adversarial training and performs well under limited system resources.

Adversarial Robustness Personalized Federated Learning +1

Hierarchical Conversational Preference Elicitation with Bandit Feedback

no code implementations6 Sep 2022 Jinhang Zuo, Songwen Hu, Tong Yu, Shuai Li, Handong Zhao, Carlee Joe-Wong

To achieve this, the recommender system conducts conversations with users, asking their preferences for different items or item categories.

Recommendation Systems

Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms

no code implementations31 Aug 2022 Xutong Liu, Jinhang Zuo, Siwei Wang, Carlee Joe-Wong, John C. S. Lui, Wei Chen

Under this new condition, we propose a BCUCB-T algorithm with variance-aware confidence intervals and conduct regret analysis which reduces the $O(K)$ factor to $O(\log K)$ or $O(\log^2 K)$ in the regret bound, significantly improving the regret bounds for the above applications.

Faithful Explanations for Deep Graph Models

no code implementations24 May 2022 Zifan Wang, Yuhang Yao, Chaoran Zhang, Han Zhang, Youjie Kang, Carlee Joe-Wong, Matt Fredrikson, Anupam Datta

Second, our analytical and empirical results demonstrate that feature attribution methods cannot capture the nonlinear effect of edge features, while existing subgraph explanation methods are not faithful.

Anomaly Detection

FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks

1 code implementation NeurIPS 2023 Yuhang Yao, Weizhao Jin, Srivatsan Ravi, Carlee Joe-Wong

Methods for training models on graphs distributed across multiple clients have recently grown in popularity, due to the size of these graphs as well as regulations on keeping data where it is generated.

Federated Learning Node Classification

FedSoft: Soft Clustered Federated Learning with Proximal Local Updating

no code implementations11 Dec 2021 Yichen Ruan, Carlee Joe-Wong

Traditionally, clustered federated learning groups clients with the same data distribution into a cluster, so that every client is uniquely associated with one data distribution and helps train a model for this distribution.

Federated Learning

GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs

no code implementations11 Oct 2021 Yucai Fan, Yuhang Yao, Carlee Joe-Wong

These works, however, do not fully address the challenge of flexibly assigning different importance to snapshots of the graph at different times, which depending on the graph dynamics may have more or less predictive power on the labels.

Classification Dynamic Node Classification +1

Combinatorial Multi-armed Bandits for Resource Allocation

1 code implementation10 May 2021 Jinhang Zuo, Carlee Joe-Wong

In doing so, the decision maker should learn the value of the resources allocated for each user from feedback on each user's received reward.

Multi-Armed Bandits

Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks

2 code implementations16 Dec 2020 Yuhang Yao, Carlee Joe-Wong

We characterize the optimal decay rate for each cluster and propose a clustering method that achieves almost exact recovery of the true clusters.

Clustering Graph Clustering +1

Can we Generalize and Distribute Private Representation Learning?

1 code implementation5 Oct 2020 Sheikh Shams Azam, Taejin Kim, Seyyedali Hosseinalipour, Carlee Joe-Wong, Saurabh Bagchi, Christopher Brinton

We study the problem of learning representations that are private yet informative, i. e., provide information about intended "ally" targets while hiding sensitive "adversary" attributes.

Federated Learning Generative Adversarial Network +2

Reconstructing Actions To Explain Deep Reinforcement Learning

no code implementations17 Sep 2020 Xuan Chen, Zifan Wang, Yucai Fan, Bonan Jin, Piotr Mardziel, Carlee Joe-Wong, Anupam Datta

Feature attribution has been a foundational building block for explaining the input feature importance in supervised learning with Deep Neural Network (DNNs), but face new challenges when applied to deep Reinforcement Learning (RL). We propose a new approach to explaining deep RL actions by defining a class of \emph{action reconstruction} functions that mimic the behavior of a network in deep RL.

Atari Games Feature Importance +2

Online Competitive Influence Maximization

no code implementations24 Jun 2020 Jinhang Zuo, Xutong Liu, Carlee Joe-Wong, John C. S. Lui, Wei Chen

In this paper, we introduce a new Online Competitive Influence Maximization (OCIM) problem, where two competing items (e. g., products, news stories) propagate in the same network and influence probabilities on edges are unknown.

Towards Flexible Device Participation in Federated Learning

no code implementations12 Jun 2020 Yichen Ruan, Xiaoxi Zhang, Shu-Che Liang, Carlee Joe-Wong

Traditional federated learning algorithms impose strict requirements on the participation rates of devices, which limit the potential reach of federated learning.

Federated Learning

Network-Aware Optimization of Distributed Learning for Fog Computing

no code implementations17 Apr 2020 Yuwei Tu, Yichen Ruan, Su Wang, Satyavrat Wagle, Christopher G. Brinton, Carlee Joe-Wong

Unlike traditional federated learning frameworks, our method enables devices to offload their data processing tasks to each other, with these decisions determined through a convex data transfer optimization problem that trades off costs associated with devices processing, offloading, and discarding data points.

Distributed, Parallel, and Cluster Computing

Machine Learning on Volatile Instances

no code implementations12 Mar 2020 Xiaoxi Zhang, Jian-Yu Wang, Gauri Joshi, Carlee Joe-Wong

Due to the massive size of the neural network models and training datasets used in machine learning today, it is imperative to distribute stochastic gradient descent (SGD) by splitting up tasks such as gradient evaluation across multiple worker nodes.

BIG-bench Machine Learning

Observe Before Play: Multi-armed Bandit with Pre-observations

no code implementations21 Nov 2019 Jinhang Zuo, Xiaoxi Zhang, Carlee Joe-Wong

We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can pay to pre-observe arm rewards before playing an arm in each round.

MOVI: A Model-Free Approach to Dynamic Fleet Management

no code implementations13 Apr 2018 Takuma Oda, Carlee Joe-Wong

Since DQNs scale poorly with a large number of possible dispatches, we streamline our DQN training and suppose that each individual vehicle independently learns its own optimal policy, ensuring scalability at the cost of less coordination between vehicles.


On the Real-time Vehicle Placement Problem

1 code implementation4 Dec 2017 Abhinav Jauhri, Carlee Joe-Wong, John Paul Shen

Motivated by ride-sharing platforms' efforts to reduce their riders' wait times for a vehicle, this paper introduces a novel problem of placing vehicles to fulfill real-time pickup requests in a spatially and temporally changing environment.


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