Timely Communications for Remote Inference
- URL: http://arxiv.org/abs/2404.16281v2
- Date: Wed, 19 Jun 2024 19:09:20 GMT
- Title: Timely Communications for Remote Inference
- Authors: Md Kamran Chowdhury Shisher, Yin Sun, I-Hong Hou,
- Abstract summary: We analyze the impact of data freshness on remote inference systems.
We propose a new "selection-from-buffer" model for sending the features.
We also design low-complexity scheduling policies to improve inference performance.
- Score: 16.671201899392585
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we analyze the impact of data freshness on remote inference systems, where a pre-trained neural network blue infers a time-varying target (e.g., the locations of vehicles and pedestrians) based on features (e.g., video frames) observed at a sensing node (e.g., a camera). One might expect that the performance of a remote inference system degrades monotonically as the feature becomes stale. Using an information-theoretic analysis, we show that this is true if the feature and target data sequence can be closely approximated as a Markov chain, whereas it is not true if the data sequence is far from being Markovian. Hence, the inference error is a function of Age of Information (AoI), where the function could be non-monotonic. To minimize the inference error in real-time, we propose a new "selection-from-buffer" model for sending the features, which is more general than the "generate-at-will" model used in earlier studies. In addition, we design low-complexity scheduling policies to improve inference performance. For single-source, single-channel systems, we provide an optimal scheduling policy. In multi-source, multi-channel systems, the scheduling problem becomes a multi-action restless multi-armed bandit problem. For this setting, we design a new scheduling policy by integrating Whittle index-based source selection and duality-based feature selection-from-buffer algorithms. This new scheduling policy is proven to be asymptotically optimal. These scheduling results hold for minimizing general AoI functions (monotonic or non-monotonic). Data-driven evaluations demonstrate the significant advantages of our proposed scheduling policies.
Related papers
- Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors [53.6277160912059]
We propose a method to promote pros of data-driven based and overfitting-based methods for better generalization, faster inference, and higher accuracy in learning neural SDFs.
We introduce a novel statistical reasoning algorithm in local regions which is able to finetune data-driven based priors without signed distance supervision, clean point cloud, or point normals.
arXiv Detail & Related papers (2024-10-25T16:48:44Z) - Causal Feature Selection via Transfer Entropy [59.999594949050596]
Causal discovery aims to identify causal relationships between features with observational data.
We introduce a new causal feature selection approach that relies on the forward and backward feature selection procedures.
We provide theoretical guarantees on the regression and classification errors for both the exact and the finite-sample cases.
arXiv Detail & Related papers (2023-10-17T08:04:45Z) - On Differential Privacy for Federated Learning in Wireless Systems with
Multiple Base Stations [90.53293906751747]
We consider a federated learning model in a wireless system with multiple base stations and inter-cell interference.
We show the convergence behavior of the learning process by deriving an upper bound on its optimality gap.
Our proposed scheduler improves the average accuracy of the predictions compared with a random scheduler.
arXiv Detail & Related papers (2022-08-25T03:37:11Z) - How Does Data Freshness Affect Real-time Supervised Learning? [15.950108699395077]
We show that the performance of real-time supervised learning degrades monotonically as the feature becomes stale.
To minimize the inference error in real-time, we propose a new "selection-from-buffer" model for sending the features.
Data-driven evaluations are presented to illustrate the benefits of the proposed scheduling algorithms.
arXiv Detail & Related papers (2022-08-15T00:14:13Z) - Continual Test-Time Domain Adaptation [94.51284735268597]
Test-time domain adaptation aims to adapt a source pre-trained model to a target domain without using any source data.
CoTTA is easy to implement and can be readily incorporated in off-the-shelf pre-trained models.
arXiv Detail & Related papers (2022-03-25T11:42:02Z) - Communication-Efficient Device Scheduling for Federated Learning Using
Stochastic Optimization [26.559267845906746]
Time learning (FL) is a useful tool in distributed machine learning that utilizes users' local datasets in a privacy-preserving manner.
In this paper, we provide a novel convergence analysis non-efficient convergence bound algorithm.
We also develop a new selection and power allocation algorithm that minimizes a function of the convergence bound and the average communication under a power constraint.
arXiv Detail & Related papers (2022-01-19T23:25:24Z) - Scheduling in Parallel Finite Buffer Systems: Optimal Decisions under
Delayed Feedback [29.177402567437206]
We present a partially observable (PO) model that captures the scheduling decisions in parallel queuing systems under limited information of delayed acknowledgements.
We numerically show that the resulting policy outperforms other limited information scheduling strategies.
We show how our approach can optimise the real-time parallel processing by using network data provided by Kaggle.
arXiv Detail & Related papers (2021-09-17T13:45:02Z) - Better than the Best: Gradient-based Improper Reinforcement Learning for
Network Scheduling [60.48359567964899]
We consider the problem of scheduling in constrained queueing networks with a view to minimizing packet delay.
We use a policy gradient based reinforcement learning algorithm that produces a scheduler that performs better than the available atomic policies.
arXiv Detail & Related papers (2021-05-01T10:18:34Z) - Unsupervised and self-adaptative techniques for cross-domain person
re-identification [82.54691433502335]
Person Re-Identification (ReID) across non-overlapping cameras is a challenging task.
Unsupervised Domain Adaptation (UDA) is a promising alternative, as it performs feature-learning adaptation from a model trained on a source to a target domain without identity-label annotation.
In this paper, we propose a novel UDA-based ReID method that takes advantage of triplets of samples created by a new offline strategy.
arXiv Detail & Related papers (2021-03-21T23:58:39Z) - PushNet: Efficient and Adaptive Neural Message Passing [1.9121961872220468]
Message passing neural networks have recently evolved into a state-of-the-art approach to representation learning on graphs.
Existing methods perform synchronous message passing along all edges in multiple subsequent rounds.
We consider a novel asynchronous message passing approach where information is pushed only along the most relevant edges until convergence.
arXiv Detail & Related papers (2020-03-04T18:15:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.