arXiv subjects

Phanwadee Sinthong

Publications and source records attributed to Phanwadee Sinthong.

5 recordsLinked to original sources

arXiv2026

Exploring CKKS Parameter Trade-offs for Privacy-Preserving Personalized Federated Learning

Privacy-preserving Personalized Federated Learning (PFL) enables clients to collaboratively train personalized models without exposing raw data, but exchanged model updates remain vulnerable to inference attacks from honest-but-curious servers. Homomorphic Encryption (HE) addresses this by allowing server-side aggregation directly on encrypted updates, with the CKKS scheme being particularly suitable due to its native support for approximate floating-point arithmetic. However, no prior work has examined how to configure CKKS for PFL deployments, leaving practitioners without principled guidance on parameter selection that directly affects privacy, precision, and computational cost. This paper presents pFedCKKS, a generic framework integrating CKKS into PFL, and provides the first systematic parameter selection guide for practitioners. We derive the full CKKS parameter constraints under 128-bit security for the PFL setting, showing the selection problem reduces to choosing just two values: the inner and outer ciphertext prime. Implemented using the Flower framework and TenSEAL library, pFedCKKS is evaluated on the FEMNIST, CelebA and Sentiment140 datasets with FedFinetune, Ditto and FedPer which represents PFL algorithms. Experimental results reveal an empirical trade-off between precision and computational/communication costs. This allows us to draw a concrete guideline for selecting proper CKKS parameters that balance efficiency and accuracy in real-world deployments of pFedCKKS.

cs.CR↗

arXiv2023

TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting

Transformers have gained popularity in time series forecasting for their ability to capture long-sequence interactions. However, their high memory and computing requirements pose a critical bottleneck for long-term forecasting. To address this, we propose TSMixer, a lightweight neural architecture exclusively composed of multi-layer perceptron (MLP) modules for multivariate forecasting and representation learning on patched time series. Inspired by MLP-Mixer's success in computer vision, we adapt it for time series, addressing challenges and introducing validated components for enhanced accuracy. This includes a novel design paradigm of attaching online reconciliation heads to the MLP-Mixer backbone, for explicitly modeling the time-series properties such as hierarchy and channel-correlations. We also propose a novel Hybrid channel modeling and infusion of a simple gating approach to effectively handle noisy channel interactions and generalization across diverse datasets. By incorporating these lightweight components, we significantly enhance the learning capability of simple MLP structures, outperforming complex Transformer models with minimal computing usage. Moreover, TSMixer's modular design enables compatibility with both supervised and masked self-supervised learning methods, making it a promising building block for time-series Foundation Models. TSMixer outperforms state-of-the-art MLP and Transformer models in forecasting by a considerable margin of 8-60%. It also outperforms the latest strong benchmarks of Patch-Transformer models (by 1-2%) with a significant reduction in memory and runtime (2-3X). The source code of our model is officially released as PatchTSMixer in the HuggingFace. Model: https://huggingface.co/docs/transformers/main/en/model_doc/patchtsmixer Examples: https://github.com/ibm/tsfm/#notebooks-links

cs.LG↗

arXiv2023

A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are served as input tokens to Transformer; (ii) channel-independence where each channel contains a single univariate time series that shares the same embedding and Transformer weights across all the series. Patching design naturally has three-fold benefit: local semantic information is retained in the embedding; computation and memory usage of the attention maps are quadratically reduced given the same look-back window; and the model can attend longer history. Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models. We also apply our model to self-supervised pre-training tasks and attain excellent fine-tuning performance, which outperforms supervised training on large datasets. Transferring of masked pre-trained representation on one dataset to others also produces SOTA forecasting accuracy. Code is available at: https://github.com/yuqinie98/PatchTST.

cs.LG↗

arXiv2021

PolyFrame: A Retargetable Query-based Approach to Scaling DataFrames (Extended Version)

In the last few years, the field of data science has been growing rapidly as various businesses have adopted statistical and machine learning techniques to empower their decision making and applications. Scaling data analysis, possibly including the application of custom machine learning models, to large volumes of data requires the utilization of distributed frameworks. This can lead to serious technical challenges for data analysts and reduce their productivity. AFrame, a Python data analytics library, is implemented as a layer on top of Apache AsterixDB, addressing these issues by incorporating the data scientists' development environment and transparently scaling out the evaluation of analytical operations through a Big Data management system. While AFrame is able to leverage data management facilities (e.g., indexes and query optimization) and allows users to interact with a very large volume of data, the initial version only generated SQL++ queries and only operated against Apache AsterixDB. In this work, we describe a new design that retargets AFrame's incremental query formation to other query-based database systems as well, making it more flexible for deployment against other data management systems with composable query languages.

cs.DB↗

arXiv2019

AFrame: Extending DataFrames for Large-Scale Modern Data Analysis (Extended Version)

Analyzing the increasingly large volumes of data that are available today, possibly including the application of custom machine learning models, requires the utilization of distributed frameworks. This can result in serious productivity issues for "normal" data scientists. This paper introduces AFrame, a new scalable data analysis package powered by a Big Data management system that extends the data scientists' familiar DataFrame operations to efficiently operate on managed data at scale. AFrame is implemented as a layer on top of Apache AsterixDB, transparently scaling out the execution of DataFrame operations and machine learning model invocation through a parallel, shared-nothing big data management system. AFrame incrementally constructs SQL++ queries and leverages AsterixDB's semistructured data management facilities, user-defined function support, and live data ingestion support. In order to evaluate the proposed approach, this paper also introduces an extensible micro-benchmark for use in evaluating DataFrame performance in both single-node and distributed settings via a collection of representative analytic operations. This paper presents the architecture of AFrame, describes the underlying capabilities of AsterixDB that efficiently support modern data analytic operations, and utilizes the proposed benchmark to evaluate and compare the performance and support for large-scale data analyses provided by alternative DataFrame libraries.

cs.DB↗