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PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization

Bingyan Liu, Yao Guo, Xiangqun Chen

arXiv:2103.01548v2cs.LGcs.AIcs.DC

TL;DR

Personalization methods often use only one client’s limited data, which may lead to bias or overfitting. PFA uses neural-network sparsity representations to group similar clients for federated adaptation. On simulated datasets and clients, it outperforms other personalization methods while ensuring privacy.

  • Problem

    Personalization methods restricted to one client may overfit or become biased because each device has extremely limited data.

    Source: §1 · p. 1

  • Method

    PFA uses neural-network sparsity representations to identify similar clients and collaboratively adapt a federated model.

    Source: §1 · p. 2, §4.1 · p. 4

  • Results

    Across simulated datasets and clients, PFA outperforms other personalization methods while ensuring privacy.

    Source: §8 · p. 11

  • Takeaways & Limitations

    The authors position federated adaptation as a new angle for personalization research in federated learning.

    Source: §8 · p. 11

    Evaluation is limited to simulated datasets and clients because recruiting enough real users to run the pipeline was infeasible.

    Source: §6 · p. 10

Abstract

from arXiv · show

Federated learning (FL) has become a prevalent distributed machine learning paradigm with improved privacy. After learning, the resulting federated model should be further personalized to each different client. While several methods have been proposed to achieve personalization, they are typically limited to a single local device, which may incur bias or overfitting since data in a single device is extremely limited. In this paper, we attempt to realize personalization beyond a single client. The motivation is that during FL, there may exist many clients with similar data distribution, and thus the personalization performance could be significantly boosted if these similar clients can cooperate with each other. Inspired by this, this paper introduces a new concept called federated adaptation, targeting at adapting the trained model in a federated manner to achieve better personalization results. However, the key challenge for federated adaptation is that we could not outsource any raw data from the client during adaptation, due to privacy concerns. In this paper, we propose PFA, a framework to accomplish Privacy-preserving Federated Adaptation. PFA leverages the sparsity property of neural networks to generate privacy-preserving representations and uses them to efficiently identify clients with similar data distributions. Based on the grouping results, PFA conducts an FL process in a group-wise way on the federated model to accomplish the adaptation. For evaluation, we manually construct several practical FL datasets based on public datasets in order to simulate both the class-imbalance and background-difference conditions. Extensive experiments on these datasets and popular model architectures demonstrate the effectiveness of PFA, outperforming other state-of-the-art methods by a large margin while ensuring user privacy. We will release our code at: https://github.com/lebyni/PFA.

1 INTRODUCTION

Personalization methods often rely on one client’s limited, non-IID data, which may introduce bias or overfitting. Federated adaptation instead selects clients with similar data distributions to collaboratively personalize a federated model; PFA uses privacy-preserving sparsity representations to support this process.

1 INTRODUCTION

  • Single-device personalization may introduce bias or overfitting because each device has extremely limited data; similar clients may contribute additional useful data.The paper presents this collaboration as a way to mitigate bias or overfitting and extend useful knowledge.Source: §1 · pp. 1–2
  • Federated adaptation selects clients with similar data distributions to collaboratively adapt a federated model, rather than adapting it with each client’s data alone.The adaptation target is a model already trained through FL, and clients are selected to match distributions.Source: §1 · p. 2
  • PFA addresses the privacy challenge by using neural-network sparsity to represent client data distributions for grouping, then conducting group-wise federated learning for adaptation.The sparsity-based representations replace raw data during client grouping.Source: §1 · p. 2
  • The paper evaluates PFA on constructed datasets with state-of-the-art model architectures and reports effectiveness, alongside convergence and privacy analyses.The introduction describes the experiments as detailed analyses of the framework’s necessity and effectiveness.Source: §1 · p. 2

2 BACKGROUND AND TERMINOLOGY

The background introduces CNN feature-map sparsity and the standard federated-learning workflow. These concepts provide the terminology used to describe the paper’s approach.

2 BACKGROUND AND TERMINOLOGY

  • The background introduces CNN and federated-learning concepts to clarify terminology and explain the workflow used in the paper.Source: §2 · p. 2

2.1 Convolutional Neural Network

  • For a ReLU output channel, sparsity is the fraction of matrix elements that are zero; the example has 9 of 16 zeros, or 56.25%.Per-channel sparsity values form a sparsity vector for the feature map.Source: §2.1 · p. 3, Figure 2 · p. 3

2.2 Federated Learning

  • Federated learning trains models locally, uploads parameters or gradients for server aggregation, and distributes the resulting global model back to clients without exchanging raw data.The local-training and server-aggregation steps may be iterated until convergence.Source: §2.2 · p. 3

3 GOAL AND CHALLENGE

PFA aims to personalize a federated model by grouping clients with similar distributions and adapting it without raw client data. The formulation and challenges center on privacy, efficient similarity measurement, and client scheduling.

3.1 Problem Formulation

  • PFA’s stated goal is to generate a privacy-preserving client representation from a traditionally trained federated model for later use in adaptation.Source: §3.1 · p. 3
  • PFA groups clients with distributions similar to a target client and adapts the federated model using representations and a similarity matrix rather than raw data.The desired personalized model is produced through federated adaptation.Source: §3.1 · p. 3

3.2 Challenges

  • Adaptation must not transfer or infer clients’ raw data, while similarity assessment must scale to many clients and identify which clients should be aggregated.Source: §3.2 · p. 3

4 OUR FRAMEWORK: PFA

PFA uses client feature-map sparsity to represent distributions, compares those representations to schedule similar clients, and adapts their models group-wise. Its design addresses privacy, comparison efficiency, and client coordination.

4 OUR FRAMEWORK: PFA

  • PFA assigns feature extraction, similarity comparison, and client scheduling to three modules that jointly support federated personalization.PFE extracts representations, FSC compares them, and CSM groups models for adaptation.Source: §4 · p. 4

4.1 Privacy-preserving Feature Extraction

  • Channel-wise sparsity from selected federated-model feature maps forms a client representation that is intended to reflect distribution while being harder to invert and smaller to upload than feature maps.The method averages sample sparsities by channel and selects q channels for the representation.Source: §4.1 · pp. 4–5

4.2 Feature Similarity Comparison

  • FSC compares sparsity vectors with Euclidean distance; a one-client-related similarity vector reduces the 100,000-client example from C^2 100,000 distance calculations to 99,999.The resulting vector is used to form the similarity matrix.Source: §4.2 · p. 5

4.3 Client Scheduling Module

  • CSM partitions clients by distribution similarity, then applies FedAvg to their local trained models within groups until each group’s adapted model fits its distribution.Clients first train locally for a few epochs, then upload models for grouping and aggregation.Source: §4.3 · pp. 5–6

5 EVALUATION

The evaluation tests PFA on simulated class-imbalance and background-difference settings, comparing personalization methods and examining representation effectiveness, privacy, and extraction choices. PFA performs better across the reported settings, while the analyses identify conditions for effective client discrimination and limits to the privacy evidence.

5.1 Experimental Setup

  • The evaluation simulates class imbalance with 25 Cifar10 clients across five distribution types and background differences with 20 Office-Home clients across four data-distribution types, using VGG-11 and MobileNetV2-0.25.Source: §5.1 · p. 6

5.2 RQ1: Overall Results

  • On Office-Home, PFA outperformed alternatives for 18/20 VGG-11 clients and 19/20 MobileNetV2-0.25 clients, with gains up to 6.90% and 8.34%, respectively.The reported gains are for client6 on VGG-11 and client15 on MobileNetV2-0.25.Source: Table 3 · p. 7, Table 4 · p. 7, §5.2 · p. 8
  • PFA achieved over 2% average improvement on both models across Office-Home domains.The reported domain-level benefit varied by domain and model architecture.Source: Table 5 · p. 7, Table 6 · p. 8, §5.2 · p. 8
  • On Cifar10, PFA consistently achieved better accuracy than other personalization methods, especially when baseline accuracy was low.Figure 5 reports accuracy improvement relative to the baseline.Source: Figure 5 · p. 8, §5.2 · p. 8

5.3 RQ2: Effectiveness of the Sparsity-based Representation

  • 7.52% and 8.10% average accuracy improvements over federated learning and random selection, respectively, were achieved by sparsity-based selection across all 20 clients.Random selection performed worse than traditional federated learning on average.Source: Table 8 · p. 8, §5.3 · p. 9

5.4 RQ3: Privacy Analysis

  • In inversion attacks, whole-feature-map sparsity produced noise-like images, while deeper-layer properties were harder to invert.The authors report that even first-ReLU-layer sparsity did not reveal useful knowledge; feature maps themselves were more readily inverted.Source: Figure 6 · p. 9, §5.4 · p. 9

5.5 RQ4: Influence on Different Extraction Strategies

  • Shallower-layer representations and more channels improved distribution distinction, with 30 channels sufficient in the reported experiment.The analysis compares eight Office-Home clients using VGG-11 and Euclidean distances between sparsity vectors.Source: §5.5 · p. 9

6 DISCUSSIONS

The discussion identifies three boundaries of PFA: its current evaluation covers CNN architectures and simulated clients, and its privacy protection lacks a formal guarantee.

More model architectures

  • PFA currently targets CNN-based architectures; extending it to RNNs and GCNs is left for future work.Source: §6 · p. 10

Formal privacy guarantee

  • The sparsity representation has no formal privacy guarantee; adding differential-privacy noise is proposed as future work.Source: §6 · p. 10

Real-world applications

  • PFA is evaluated only on simulated datasets and clients, since recruiting enough real client users to run the pipeline is infeasible for the authors.The simulation design is based on real-world scenarios.Source: §6 · p. 10

7 RELATED WORK

Prior FL personalization methods include clustering and local adaptation, while PFA uses federated adaptation to leverage knowledge across clients without uploading raw data.

Federated Learning and Personalization

  • Earlier personalization approaches include clustering similar users and local fine-tuning, multi-task learning, or knowledge distillation.The cited clustering method requires users' raw data for clustering, while local approaches do not exchange user data.Source: §7 · p. 10
  • PFA differs from local personalization by using federated adaptation to draw on knowledge from other clients without uploading raw data.The paper frames this as addressing overfitting or training bias during personalization.Source: §7 · p. 10

Sparsity of CNNs

  • Unlike prior CNN sparsity work aimed at accelerating computation, this paper uses sparsity to distinguish clients with diverse data distributions.Source: §7 · p. 10

Privacy Protection of User Data

  • The paper notes secure multiparty computation's client-side computation and communication costs, and leaves adding differential-privacy noise to its representation for future work.Source: §7 · p. 10

8 CONCLUSION

The paper introduces federated adaptation to extend personalization beyond a single device and presents PFA as a privacy-preserving framework for it. Experiments on simulated datasets and clients show PFA outperforming other personalization methods while ensuring privacy.

8 CONCLUSION

  • PFA uses a sparsity vector to compare and schedule clients for adaptation.This identifies suitable federated clients to participate.Source: §8 · p. 11
  • Federated adaptation extends existing personalization techniques beyond a single device, and the paper presents it as a new direction for FL research.The authors describe this as the first work to study and achieve federated adaptation.Source: §8 · p. 11
  • Experiments on simulated datasets and clients show PFA outperforming other personalization methods while ensuring privacy.Source: §8 · p. 11

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