Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning

• URL: http://arxiv.org/abs/2607.29353v1
• Date: Fri, 31 Jul 2026 12:37:48 GMT
• Title: Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning
• Abstract summary: We introduce embedder-centric learning (ECL), a framework that unifies four different online learning scenarios.<n>ECL can be deployed on resource-constrained devices across four real-world use cases.<n>We present first hardware demonstrations of ZSL with semantic data and ICL operating at micro-to-milliwatt power budgets.
• Score: 0.05402395391469584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the ever-increasing pervasiveness of smart edge devices, the demand is growing for applications that can be tailored to users (e.g., custom keyword spotting) or patients (e.g., adaptive health monitoring). Yet, most edge devices rely on fixed inference algorithms and thus cannot learn on-device to personalize predictions. When they can, devices typically support only a specific learning scenario, such as few-shot learning (FSL): going beyond this requires resorting either to another specialized device or to cloud-based retraining, which implies significant energy and latency overheads, a lack of real-time capabilities, and privacy concerns. In this work, we introduce embedder-centric learning (ECL), a framework that unifies four different online learning scenarios: FSL for on-the-fly customization, continual learning (CL) for knowledge accumulation, zero-shot learning (ZSL) for leveraging semantic data, and in-context learning (ICL) for adapting beyond classification. We demonstrate in silicon that ECL can be deployed on resource-constrained devices across four real-world use cases representative of the aforementioned learning scenarios. Our approach establishes a new state-of-the-art performance for FSL character recognition (Omniglot: 96.8% for 5-way 1-shot, 83.3% for 32-way 1-shot), and the first hardware baseline for CL in keyword spotting (NeuroBench keyword FSCIL: 71.8% for 200-way 5-shot). Moreover, we present the first hardware demonstrations of ZSL with semantic data (60.6% for 5-way spoken sentence classification) and ICL (46.2% at the 500th token of RegBench) operating at micro-to-milliwatt power budgets. Therefore, by unifying multiple learning scenarios, we pave the way for smart and versatile devices that can adapt right at the edge, without reliance on the cloud.

Related papers

• Private PoEtry: Private In-Context Learning via Product of Experts [58.496468062236225]
  In-context learning (ICL) enables Large Language Models to adapt to new tasks with only a small set of examples at inference time.<n>Existing differential privacy approaches to ICL are either computationally expensive or rely on oversampling, synthetic data generation, or unnecessary thresholding.<n>We reformulate private ICL through the lens of a Product-of-Experts model. This gives a theoretically grounded framework, and the algorithm can be trivially parallelized.<n>We find that our method improves accuracy by more than 30 percentage points on average compared to prior DP-ICL methods, while maintaining strong privacy guarantees.
  arXiv  Detail & Related papers  (2026-02-04T19:56:24Z)
• CLA: Latent Alignment for Online Continual Self-Supervised Learning [53.52783900926569]
  We introduce Continual Latent Alignment (CLA), a novel SSL strategy for Online CL.<n>Our CLA is able to speed up the convergence of the training process in the online scenario, outperforming state-of-the-art approaches under the same computational budget.<n>We also discovered that using CLA as a pretraining protocol in the early stages of pretraining leads to a better final performance when compared to a full i.i.d. pretraining.
  arXiv  Detail & Related papers  (2025-07-14T16:23:39Z)
• Chameleon: A MatMul-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data [0.15178034047411867]
  On-device learning at the edge enables low-latency, private personalization with improved long-term robustness and reduced maintenance costs.<n> achieving scalable, low-power end-to-end on-chip learning, especially from real-world sequential data with a limited number of examples is an open challenge.<n>We present Chameleon, leveraging three key contributions to solve these challenges.
  arXiv  Detail & Related papers  (2025-05-30T17:49:30Z)
• TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservation [49.801175302937246]
  This study provides insights into deploying privacy-preserving, energy-efficient NLP models on edge devices.<n>We introduce semantic split learning (SL) as an energy-efficient, privacy-preserving tiny machine learning (TinyML) framework.<n>Our results show that SL significantly reduces computational power and CO2 emissions while enhancing privacy, as evidenced by a fourfold increase in reconstruction error compared to FL and nearly eighteen times that of CL.
  arXiv  Detail & Related papers  (2024-11-09T21:26:59Z)
• CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication [5.636155173401658]
  Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies for training a decentralized Machine Learning (ML) model. We propose combining these technologies to address the continuous authentication challenge while protecting user privacy.
  arXiv  Detail & Related papers  (2024-05-12T06:08:21Z)
• Boosting keyword spotting through on-device learnable user speech characteristics [16.632185274553485]
  Keywords spotting systems for always-on TinyML-constrained applications require on-site tuning. Adapting to the speech peculiarities of target users requires many in-domain samples, often unavailable in real-world scenarios. We propose a novel on-device learning architecture, composed of a pretrained backbone and a user-aware embedding learning the user's speech characteristics.
  arXiv  Detail & Related papers  (2024-03-12T16:41:31Z)
• LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms [17.031135153343502]
  Continual Learning (CL) allows applications such as user personalization and household robots to learn on the fly and adapt to context. LifeLearner is a hardware-aware meta learning system that drastically optimize system resources. LifeLearner achieves near-optimal CL performance, falling short by only 2.8% on accuracy compared to an Oracle baseline.
  arXiv  Detail & Related papers  (2023-11-19T20:39:35Z)
• Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly [62.473245910234304]
  This paper takes a hardware-centric approach to explore how Large Language Models can be brought to modern edge computing systems. We provide a micro-level hardware benchmark, compare the model FLOP utilization to a state-of-the-art data center GPU, and study the network utilization in realistic conditions.
  arXiv  Detail & Related papers  (2023-10-04T20:27:20Z)
• FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems [61.335229621081346]
  Federated Learning (FL) has become a viable technique for realizing privacy-enhancing distributed deep learning on the network edge. In this paper, we propose FLEdge, which complements existing FL benchmarks by enabling a systematic evaluation of client capabilities.
  arXiv  Detail & Related papers  (2023-06-08T13:11:20Z)
• Semi-Supervised Federated Learning for Keyword Spotting [15.044022869136262]
  Keywords Spotting (KWS) is a critical aspect of audio-based applications on mobile devices and virtual assistants. Recent developments in Federated Learning (FL) have significantly expanded the ability to train machine learning models.
  arXiv  Detail & Related papers  (2023-05-09T00:46:12Z)
• A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays [66.62377866022221]
  Latent Replay-based Continual Learning (CL) techniques enable online, serverless adaptation in principle. We introduce a HW/SW platform for end-to-end CL based on a 10-core FP32-enabled parallel ultra-low-power processor. Our results show that by combining these techniques, continual learning can be achieved in practice using less than 64MB of memory.
  arXiv  Detail & Related papers  (2021-10-20T11:01:23Z)

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.