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Found 16 papers, 4 papers with code
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks
In this paper, we provide a convergence analysis of SFL which quantifies the impact of model splitting (MS) and client-side model aggregation (MA) on the learning performance, serving as a theoretical foundation.
Identification of Additive Link Metrics: Proof of Selected Theorems
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
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
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
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Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing
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
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.
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach
Spatial crowdsourcing (SC) utilizes the potential of a crowd to accomplish certain location based tasks.
Overcoming Noisy and Irrelevant Data in Federated Learning
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.
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach
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.
Fast-Fourier-Forecasting Resource Utilisation in Distributed Systems
To address the first challenge, we present a communication-efficient data collection mechanism.
Model Pruning Enables Efficient Federated Learning on Edge Devices
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.
MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design
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.
Energy-Efficient Radio Resource Allocation for Federated Edge Learning
To reduce devices' energy consumption, we propose energy-efficient strategies for bandwidth allocation and scheduling.
Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems
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.
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches.
Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process
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
