Search Results for author: Kin K. Leung

Found 15 papers, 4 papers with code

Identification of Additive Link Metrics: Proof of Selected Theorems

no code implementations18 Dec 2020 Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, Don Towsley

This is a technical report, containing all the theorem proofs in the following two papers: (1) Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley, "Identifiability of Link Metrics Based on End-to-end Path Measurements," in ACM IMC, 2013.

Networking and Internet Architecture

Node Failure Localization: Theorem Proof

no code implementations17 Dec 2020 Liang Ma, Ting He, Ananthram Swami, Don Towsley, Kin K. Leung

This is a technical report, containing all the theorem proofs in paper "On Optimal Monitor Placement for Localizing Node Failures via Network Tomography" by Liang Ma, Ting He, Ananthram Swami, Don Towsley, and Kin K. Leung, published in IFIP WG 7. 3 Performance, 2015.

Networking and Internet Architecture

Jointly-Learned State-Action Embedding for Efficient Reinforcement Learning

no code implementations9 Oct 2020 Paul J. Pritz, Liang Ma, Kin K. Leung

While reinforcement learning has achieved considerable successes in recent years, state-of-the-art models are often still limited by the size of state and action spaces.

Model-based Reinforcement Learning Recommendation Systems +2

Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing

no code implementations14 Jul 2020 Qunsong Zeng, Yuqing Du, Kaibin Huang, Kin K. Leung

Among others, the framework of federated edge learning (FEEL) is popular for its data-privacy preservation.

Information Theory Signal Processing Information Theory

State Action Separable Reinforcement Learning

no code implementations5 Jun 2020 Ziyao Zhang, Liang Ma, Kin K. Leung, Konstantinos Poularakis, Mudhakar Srivatsa

We observe that although actions directly define the agents' behaviors, for many problems the next state after a state transition matters more than the action taken, in determining the return of such a state transition.

Decision Making reinforcement-learning +1

Overcoming Noisy and Irrelevant Data in Federated Learning

no code implementations22 Jan 2020 Tiffany Tuor, Shiqiang Wang, Bong Jun Ko, Changchang Liu, Kin K. Leung

A challenge is that among the large variety of data collected at each client, it is likely that only a subset is relevant for a learning task while the rest of data has a negative impact on model training.

Federated Learning

Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach

no code implementations14 Jan 2020 Pengchao Han, Shiqiang Wang, Kin K. Leung

Then, with the goal of minimizing the overall training time, we propose a novel online learning formulation and algorithm for automatically determining the near-optimal communication and computation trade-off that is controlled by the degree of gradient sparsity.

Fairness Federated Learning

Fast-Fourier-Forecasting Resource Utilisation in Distributed Systems

no code implementations13 Jan 2020 Paul J. Pritz, Daniel Perez, Kin K. Leung

To address the first challenge, we present a communication-efficient data collection mechanism.

Distributed Computing Scheduling

Model Pruning Enables Efficient Federated Learning on Edge Devices

2 code implementations26 Sep 2019 Yuang Jiang, Shiqiang Wang, Victor Valls, Bong Jun Ko, Wei-Han Lee, Kin K. Leung, Leandros Tassiulas

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.

Federated Learning

MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design

no code implementations19 Sep 2019 Ziyao Zhang, Liang Ma, Konstantinos Poularakis, Kin K. Leung, Jeremy Tucker, Ananthram Swami

In distributed software-defined networks (SDN), multiple physical SDN controllers, each managing a network domain, are implemented to balance centralised control, scalability, and reliability requirements.

reinforcement-learning Reinforcement Learning (RL)

Energy-Efficient Radio Resource Allocation for Federated Edge Learning

no code implementations13 Jul 2019 Qunsong Zeng, Yuqing Du, Kin K. Leung, Kaibin Huang

To reduce devices' energy consumption, we propose energy-efficient strategies for bandwidth allocation and scheduling.

Management Scheduling

Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems

no code implementations22 May 2019 Tiffany Tuor, Shiqiang Wang, Kin K. Leung, Bong Jun Ko

Monitoring the conditions of these nodes is important for system management purposes, which, however, can be extremely resource demanding as this requires collecting local measurements of each individual node and constantly sending those measurements to a central controller.

Anomaly Detection Distributed Computing +2

Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process

1 code implementation17 Jun 2015 Shiqiang Wang, Rahul Urgaonkar, Murtaza Zafer, Ting He, Kevin Chan, Kin K. Leung

In mobile edge computing, local edge servers can host cloud-based services, which reduces network overhead and latency but requires service migrations as users move to new locations.

Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Optimization and Control

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