Learning to Help in Multi-Class Settings
Yu Wu, Yansong Li, Zeyu Dong, Nitya Sathyavageeswaran, Anand D., Sarwate
TL;DR
This paper extends the Learning to Help (L2H) model to multi-class classification, enabling resource-efficient hybrid models that balance local computation and server assistance, suitable for constrained devices.
Contribution
It introduces a multi-class L2H framework with a novel differentiable surrogate loss, expanding its applicability and effectiveness in practical resource-limited scenarios.
Findings
The multi-class L2H model performs well in resource-constrained environments.
The surrogate loss is convex, differentiable, and Bayes-consistent.
Experiments demonstrate improved efficiency and practicality.
Abstract
Deploying complex machine learning models on resource-constrained devices is challenging due to limited computational power, memory, and model retrainability. To address these limitations, a hybrid system can be established by augmenting the local model with a server-side model, where samples are selectively deferred by a rejector and then sent to the server for processing. The hybrid system enables efficient use of computational resources while minimizing the overhead associated with server usage. The recently proposed Learning to Help (L2H) model trains a server model given a fixed local (client) model, differing from the Learning to Defer (L2D) framework, which trains the client for a fixed (expert) server. In both L2D and L2H, the training includes learning a rejector at the client to determine when to query the server. In this work, we extend the L2H model from binary to…
Peer Reviews
Decision·ICLR 2025 Poster
The setting of learning to help is quite interesting and meaningful. The authors extend it to multi-class classification cases, which have extensive applications. The proposed method is theoretically guaranteed. Also, the presentation is good.
The main drawbacks of this paper may be in the experiment part. - The datasets used are small, such as CIFAR-10 and SVHN. And there are only two datasets. Usually, evaluation on 3 datasets (or more) will be better. - The used LeNet and AlexNet are tiny. Trending sota models like ViT can be considered. - The learning to help scenarios can be very useful in large language model settings for on-device inference. Can the authors do some experiments on this? Or at least give a discussion. - How do yo
- The writing of this manuscript is clear, and easy to follow. - This manuscript addresses three practical scenarios reflecting constraints related to cost, availability, and policy, and derives formal objective functions to represent them.
- The authors state that they extend the “learning to help” framework to handle multi-class classification but do not explain the challenges involved in transitioning from binary-class to multi-class classification. - In the Conclusion section, the authors state that the proposed framework opens new avenues for further exploration in multi-party collaboration. However, since the evaluation uses relatively simple networks, such as LeNet-5 and AlexNet, the authors should include more complex netwo
1. The theoretical analysis of this paper is solid, which extends the binary classification setting of L2H to general multi-class settings. 2. The problem is important and the proposed method uses a surrogate loss to asynchronously train client and server model, which is simple yet theoretically effective.
The experiments are limited. Experiments are mainly composed of hyper-parameter sensitive analysis evaluations, which lack comparison to baselines. For example, this paper should compare the proposed method with those using the synchronous training method or different surrogate loss functions in terms of overall performance.
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Taxonomy
TopicsEducational and Psychological Assessments · Collaborative Teaching and Inclusion · Family and Disability Support Research