FedPS: Federated Preprocessing for structured data via aggregated Statistics

arXiv:2602.10870 · cs.LG, cs.AI · Submitted 2026-02-11 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "FedPS: Federated Preprocessing for structured data via aggregated Statistics".

Jane: The paper was written by Xuefeng Xu and Graham Cormode from University of Warwick and University of Oxford.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Summary: Tom: So, in this section, the authors summarize their approach in "FedPS: Federated Preprocessing for structured data via aggregated Statistics." They describe a multi-step process that is designed to be both consistent and private.

Jane: Think of it as a sophisticated bridge between the raw data clients hold and the final model training. The process outlined is very methodical, starting with local computation on each client.

Lu: Each client first computes local statistics, which are then sent to the server for aggregation—this is Step one and Step two in their framework. It's a crucial distinction from simply assuming clean data exists.

Meng: The server takes those aggregated statistics and derives the necessary preprocessing parameters—Step three. This part of the management is what makes this approach practical, ensuring we don't need to see all the raw data simultaneously.

Lalam: Then, Step four is when broadcasting those derived parameters back to Step five which applies the transformation locally on each client. This whole cycle ensures that everyone is working with a consistent set of rules.

Tom: That entire workflow makes sense, Jane, because it manages the tension between privacy and consistency really well. It's not just random local application; it's coordinated preparation across multiple data silos.

Jane: I think that coordination is what will resonate most with the listeners who are dealing with non-IID data silos—meaning different clients have vastly different distributions of their own data.

Lu: The structure of this framework, as presented in "FedPS: Federated Preprocessing for structured data via aggregated Statistics," allows us to handle complexity while keeping the process manageable.

Improvements/Methods: Tom: We've talked about the general flow, but now we need to look deeper into how FedPS handles different preprocessing tasks. The paper is incredibly broad in its coverage of methods.

Jane: It really covers everything from simple scaling to much more complex things like imputation and transformation, which are often overlooked in other existing tools.

Lu: For example, they aren't just doing basic standardization; they have robust solutions for handling outliers using quantiles through RobustScaler and QuantileTransformer.

Meng: And I'm interested in the implementation of methods like Federated Bayesian Linear Regression—how it’s applied to IterativeImputer. This is a sophisticated way to handle missing values without centralized data access.

Lalam: It sounds like the ability to handle both horizontal and vertical FL settings with these techniques is a massive improvement, Lu, because real-world data setups are rarely just one type of partition.

Tom: That's key, Lalam. The authors have managed to extend core models—like k-Means and Bayesian Linear Regression—to support this federated preprocessing workflow in "FedPS: Federated Preprocessing for structured data via aggregated Statistics."

Jane: It’s a testament to the flexibility that you can take these established ML techniques and adapt them for distributed environments.

Lu: By showing how they handle things like K-Means clustering in a federated manner, it opens up possibilities for finding patterns across different local datasets.

Meng: The implementation of KNNImputer using k-NN regression is also very practical, and the way they structure the communication flow for that will be crucial in deployment.

Communication Overhead: Tom: Now, let's talk about costs. Since we are talking about a practical framework, communication overhead is paramount in "FedPS: Federated Preprocessing for structured data via aggregated Statistics."

Jane: That's where the engineering side of things gets really interesting. We see that not all methods require the same level of information exchange, which is a vital distinction.

Lu: The paper provides a detailed analysis of communication overhead in Section three point two, showing exactly how different statistical primitives translate into costs per client under both horizontal and vertical partitioning.

Meng: This is where my team will get real value; seeing the asymptotic cost functions—like O(m) versus O(n'km)—helps us choose the right tool for the job based on our data scale.

Lalam: The fact that they use data-sketching techniques to keep these costs low is impressive, Lalam. It suggests we don't have to sacrifice accuracy for efficiency.

Tom: It sounds like they've managed to quantify the trade-offs between complexity and communication requirements really well in "FedPS: Federated Preprocessing for structured data via aggregated Statistics."

Jane: The contrast between a simple statistic needing minimal communication and a complex operation involving iterative imputation is quite stark.

Lu: It’s clear that the design choices made by the authors are highly informed by how these different operations scale computationally.

Meng: I'm particularly interested in how they quantify those costs for an implementation where we might be dealing with thousands of features, to see if the theoretical limits hold up in practice.

Conclusion: Tom: We’ve covered a lot of ground today, from the core philosophy of "FedPS: Federated Preprocessing for structured data via aggregated Statistics" to its detailed communication analysis. We're getting ready to wrap things up.

Jane: I think we can conclude that this work provides a robust, systematic solution that is genuinely missing in existing federated learning tools and greatly benefit practitioners.

Lu: The ability the authors demonstrate to handle non-IID data distributions is perhaps the most profound implication of the paper, Lu, as real-world data rarely follows a neat distribution.

Meng: I think for my team, this means we can finally build reliable production systems that manage preprocessing complexity without breaking our budget or compromising privacy.

Lalam: The advancement in AI through FedPS allows us to build tools that are not only smart but also operationally sound, improving how we handle complex data environments.

Tom: It seems like a comprehensive solution, Jane, and it's definitely something we can rely on again when we discuss the future work of "FedPS: Federated Preprocessing for structured data via aggregated Statistics."

Jane: It’s been a fantastic discussion with all of you today. We hope our listeners feel more confident in the power of federated preprocessing!

University of Warwick · University of Oxford

cs.LG, cs.AI

Submitted: 2026-02-11

Updated: 2026-09-03

Comments: TMLR 2026. 27 pages, 8 figures, 7 tables. Project page see http://xuefeng-xu.github.io/fedps.html

Code: https://github.com/xuefeng-xu/fedps

Project page: http://xuefeng-xu.github.io/fedps.html

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 88/100

The gist: The paper "FedPS: Federated Preprocessing for structured data via aggregated Statistics" addresses the critical challenge of adapting standard machine learning preprocessing pipelines for

Key concepts

FedPS: It is a multi-step framework designed to be consistent and private when handling raw data clients hold. The process involves local computation, sending statistics to the server for aggregation, deriving necessary preprocessing parameters, and broadcasting those parameters back to apply the transformation locally.
Non-IID Data: This refers to real-world data silos where different clients have vastly different distributions of their own data. The FedPS framework is specifically designed to handle this complexity, allowing patterns to be found across these varied local datasets.
Communication Overhead: This refers to the cost of information exchange in a practical framework. The paper analyzes how different statistical primitives translate into costs per client under various partitioning settings, using data-sketching techniques to keep these costs low while maintaining accuracy.

Terminology

Federated Learning — 25× in this paper · explained in 14 episodes
Accuracy — 24× in this paper · explained in 1 episode
Aggregation — 17× in this paper · explained in 1 episode
Bayesian Linear Regression — 12× in this paper
Clustering — 8× in this paper · explained in 2 episodes
Communication Overhead — 7× in this paper · explained in 2 episodes
Non-IID Data — 7× in this paper · explained in 2 episodes
Consistency — 5× in this paper · explained in 3 episodes
Core — 5× in this paper · explained in 2 episodes
Iterative Refinement — 5× in this paper · explained in 3 episodes
Logistic Regression — 4× in this paper
Unified Framework — 4× in this paper · explained in 5 episodes
Adam — 2× in this paper · explained in 2 episodes
Data Heterogeneity — 2× in this paper · explained in 3 episodes
Federated Learning (FL) — 2× in this paper · explained in 7 episodes
Alignment — 1× in this paper · explained in 1 episode
Bayesian Inference — 1× in this paper · explained in 5 episodes
Distribution Shift — 1× in this paper · explained in 5 episodes
Feature Engineering — 1× in this paper · explained in 3 episodes
Generalization — 1× in this paper · explained in 13 episodes
Interpretability — 1× in this paper · explained in 15 episodes
Multi Layer Perceptron — 1× in this paper
Quantization — 1× in this paper · explained in 12 episodes
Reconstruction Error — 1× in this paper · explained in 2 episodes
Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Summary

The paper FedPS: Federated Preprocessing for structured data via aggregated Statistics addresses the critical challenge of adapting standard machine learning preprocessing pipelines for decentralized, federated learning environments. Since clients hold local data silos and cannot share raw datasets, accurate feature engineering requires sophisticated methods to aggregate necessary global statistics—such as quantiles, sums, and variances—while minimizing communication overhead.

Scaling and Normalization Techniques

Federated scaling methods must adapt classical techniques to utilize aggregated statistics. For example:

RobustScaler: This method uses quantiles (Q1, Q2, Q3) to reduce sensitivity to outliers. The scaling rule applied is (x - Q2) / (Q3 - Q1).
Normalizer: This rescales each data sample to have a unit norm (1, 2, or max norm). In vertical federation, the global norm must be computed by summing partial norms or taking the maximum across clients, and then dividing each feature value by this global norm: x / x.
StandardScaler: This standardizes data to obtain zero mean and unit variance.

Advanced Encoding and Transformation Methods

The paper details complex methods that require global knowledge for accurate transformation.

TargetEncoder: This encoder assigns a value to each category based on the distribution of the target Y. For binary labels, the encoded value involves a shrinkage parameter lambda(n i) = m+n and requires computing global per-category means and variances of the target.
QuantileTransformer: A non-parametric method that maps data to a Uniform or Gaussian distribution. Both transformations require global quantiles, computed via a quantile sketch.
PowerTransformer: This parametric method aims to make data more Gaussian. The parameter lambda is estimated by maximizing the log-likelihood, which necessitates global sums and variances of the transformed data.
SplineTransformer: This constructs B-spline bases, following a procedure where knot positions are chosen uniformly using global minimum and maximum values or along the global quantiles.

Handling Missing Values and Discretization

Preprocessing also involves managing categorical data, missing values, and continuous variable binning.

SimpleImputer: This univariate method addresses missing values by replacing them with the feature mean, median, or most-frequent value. To function federatedly, means require global sums and counts, while medians use the quantile sketch.
KBinsDiscretizer: This converts continuous variables into discrete categories. It supports discretization using various methods:

Uniform binning (based on global min/max).
Quantile binning (using global quantiles).
K-means clustering (using global data structure).

Federated Implementation and Efficiency

The feasibility of these techniques is measured by communication cost and framework support. Table 5 highlights the Communication cost per client for various preprocessors across different datasets, showing that methods like TargetEncoder can incur significant costs (e.g., 73.46 KB for the Adult dataset). Furthermore, Table 6 provides a comprehensive overview of framework support, indicating that:

The StandardScaler and RobustScaler are widely supported across major frameworks (FATE, SecretFlow, Baunsgaard et al., FedPS).
The PowerTransformer, QuantileTransformer, and SplineTransformer are all supported by FedPS.
The most comprehensive support is seen for the core scaling methods: MaxAbsScaler, MinMaxScaler, StandardScaler, RobustScaler, Normalizer.

Improvements for AI systems

This research paper provides a comprehensive, state-of-the-art taxonomy of federated data preprocessing techniques. Given the critical nature of data leakage, communication overhead, and non-IID robustness in real-world FL deployments, I propose developing an Adaptive Federated Preprocessing Orchestrator (AFPO) system.

The AFPO will not simply use these preprocessors; it will intelligently select, combine, and optimize them based on runtime metadata (data distribution profile, feature type, communication budget) and the specific learning task.

Here are the highly specific improvements to the AI system and what the resulting architecture can achieve:

The AFPO functions as a meta-learning layer situated before model training begins. It replaces manual selection of preprocessors with a dynamic, multi-objective optimization pipeline.

Improvement: Implement a resource-constrained optimization algorithm (e.g., using Mixed Integer Linear Programming) that treats the communication cost (Table 5) and computational complexity as primary constraints.

How it Works: When initialized, the AFPO accepts a target communication budget (B max) and a set of feature profiles. It then calculates the Pareto front of possible preprocessor combinations (e.g., P = p 1, p 2,) such that sum Cost(p i) B max.

Improved Capability: The system can guarantee that the chosen preprocessing pipeline is not only mathematically sound but also deployable within strict network bandwidth and latency constraints. It moves FL from being merely federated to being communication-aware federated.

Improvement: Replace the current single-stage transformation pipeline with a multi-modal, adaptive stack that selects the optimal transformation (T) for each feature (x i) based on its empirical distribution function (EDF).

How it Works:

Distribution Profiling: For each feature x i, the AFPO calculates global statistics (skewness, kurtosis, IQR) using sketches.
Selection Logic: If x i is highly skewed and non-Gaussian (high kurtosis), T defaults to QuantileTransformer (Gaussian output) or PowerTransformer. If the data is already close to Gaussian or has severe outliers, T prioritizes RobustScaler over standard scaling.
Combination: For optimal performance, the system can stack transformations: e.g., x'i = StandardScaler(PowerTransformer(x i)).

Improved Capability: The AFPO minimizes the information loss associated with discarding distribution characteristics (e.g., preserving relative ranks using QuantileTransformer while still achieving zero mean/unit variance via subsequent scaling). This significantly boosts model robustness in highly heterogeneous (non-IID) environments.

Improvement: Centralize and formalize the aggregation of complex sketches, particularly for TargetEncoder and quantile-based methods (RobustScaler, QuantileTransformer).

How it Works: Instead of relying on simple averaging, the AFPO implements Federated Moment Matching (FMM). For a feature x i requiring global variance (sigma squared) or mean (mu), clients submit second-order moment sketches. The central server aggregates these sketches using secure aggregation techniques (e.g., Homomorphic Encryption) to calculate mu global and sigma 2 global with provable privacy guarantees, mitigating the risk of data leakage inherent in transmitting raw statistics.

Improved Capability: This ensures that the calculation of highly sensitive metrics—like the shrinkage parameter lambda(n i) for TargetEncoder or the variance needed for RobustScaler—is mathematically sound, cryptographically protected, and minimizes statistical bias arising from client participation rates.

Improvement: Implement a dynamic module that determines if categorical features require simple one-hot encoding (OneHotEncoder) or if they benefit from continuous value representation (TargetEncoder).

How it Works: The AFPO calculates the Informational Entropy of each categorical feature C j.

If H(C j) is low (few unique values, high correlation with target), the system prioritizes TargetEncoder for its dimensionality reduction and ability to capture predictive power based on conditional probability P(YC j).
If H(C j) is high (many unique values, sparse data), the system defaults to OneHotEncoder but applies a sophisticated rare-item grouping mechanism using the frequent-item sketch to prevent dimensionality explosion and maintain model efficiency.

Improved Capability: The system autonomously selects between methods that are dimensionally explosive (OHE) and those that risk target leakage (Target Encoding), providing optimal feature representation for the given dataset structure.

Area Current Limitation Addressed AFPO Improvement Achieved Quantitative Benefit

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Deployment (Table 5) Manual selection; ignoring communication cost. High risk of network failure/overload. Dynamic Resource-Constrained Orchestration Module. Guarantees feasibility within B max. Reduced operational cost and guaranteed deployment

Sources

CleanAgent: Automating Data Standardization with LLM-based Agents