On-device transfer learning is crucial for adapting a common backbone model
to the unique environment of each edge device. Tiny microcontrollers, such as
the Raspberry Pi Pico, are key targets for on-device learning but often lack
floating-point units, necessitating integer-only training. Dynamic computation
of quantization scale factors, which is adopted in former studies, incurs high
computational costs. Therefore, this study focuses on integer-only training
with static scale factors, which is challenging with existing training methods.
We propose a new training method named PRIOT, which optimizes the network by
pruning selected edges rather than updating weights, allowing effective
training with static scale factors. The pruning pattern is determined by the
edge-popup algorithm, which trains a parameter named score assigned to each
edge instead of the original parameters and prunes the edges with low scores
before inference. Additionally, we introduce a memory-efficient variant,
PRIOT-S, which only assigns scores to a small fraction of edges. We implement
PRIOT and PRIOT-S on the Raspberry Pi Pico and evaluate their accuracy and
computational costs using a tiny CNN model on the rotated MNIST dataset and the
VGG11 model on the rotated CIFAR-10 dataset. Our results demonstrate that PRIOT
improves accuracy by 8.08 to 33.75 percentage points over existing methods,
while PRIOT-S reduces memory footprint with minimal accuracy loss.

DOI： 10.1109/LES.2024.3485003

arXiv

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その他リンク： http://arxiv.org/pdf/2503.16860v1

掲載種別：論文集(書籍)内論文  

DOI： 10.1007/978-981-97-1274-8_13

Web of Science

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1587/nolta.14.639

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1587/transinf.2022pap0001

Web of Science

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1587/NOLTA.12.526

Web of Science

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Federated learning (FL) enables multiple clients to collaboratively train
machine learning models without sharing local data. In particular,
decentralized FL (DFL), where clients exchange models without a central server,
has gained attention for mitigating communication bottlenecks. Evaluating
participant contributions is crucial in DFL to incentivize active participation
and enhance transparency. However, existing contribution evaluation methods for
FL assume centralized settings and cannot be applied directly to DFL due to two
challenges: the inaccessibility of each client to non-neighboring clients'
models, and the necessity to trace how contributions propagate in conjunction
with peer-to-peer model exchanges over time. To address these challenges, we
propose TRIP-Shapley, a novel contribution evaluation method for DFL.
TRIP-Shapley formulates the clients' overall contributions by tracing the
propagation of the round-wise local contributions. In this way, TRIP-Shapley
accurately reflects the delayed and gradual influence propagation, as well as
allowing a lightweight coordinator node to estimate the overall contributions
without collecting models, but based solely on locally observable contributions
reported by each client. Experiments demonstrate that TRIP-Shapley is
sufficiently close to the ground-truth Shapley value, is scalable to
large-scale scenarios, and remains robust in the presence of dishonest clients.

arXiv

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その他リンク： http://arxiv.org/pdf/2505.23246v2

This paper proposes a federated learning framework designed to achieve
\textit{relative fairness} for clients. Traditional federated learning
frameworks typically ensure absolute fairness by guaranteeing minimum
performance across all client subgroups. However, this approach overlooks
disparities in model performance between subgroups. The proposed framework uses
a minimax problem approach to minimize relative unfairness, extending previous
methods in distributionally robust optimization (DRO). A novel fairness index,
based on the ratio between large and small losses among clients, is introduced,
allowing the framework to assess and improve the relative fairness of trained
models. Theoretical guarantees demonstrate that the framework consistently
reduces unfairness. We also develop an algorithm, named \textsc{Scaff-PD-IA},
which balances communication and computational efficiency while maintaining
minimax-optimal convergence rates. Empirical evaluations on real-world datasets
confirm its effectiveness in maintaining model performance while reducing
disparity.

arXiv

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その他リンク： http://arxiv.org/pdf/2411.01161v1