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On Safeguarding Privacy and Security in the Framework of Federated Learning

Chuan Ma, Jun Li, Ming Ding, Howard Hao Yang, Feng Shu, Tony Q. S. Quek, H. Vincent Poor

arXiv:1909.06512v2cs.NI

TL;DR

Federated learning aims to preserve privacy by separating local data provision from centralized model aggregation, yet attacks can still expose information and manipulate training. The paper analyzes these privacy and security issues, examines convergence, poisoning, scaling, and aggregation, and discusses possible protections illustrated by simulations. It concludes that FL design must address these challenges across client, server, and system levels.

Abstract

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Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL). Specifically, FL allows a decoupling of data provision at UEs and ML model aggregation at a central unit. By training model locally, FL is capable of avoiding data leakage from the UEs, thereby preserving privacy and security to some extend. However, even if raw data are not disclosed from UEs, individual's private information can still be extracted by some recently discovered attacks in the FL architecture. In this work, we analyze the privacy and security issues in FL, and raise several challenges on preserving privacy and security when designing FL systems. In addition, we provide extensive simulation results to illustrate the discussed issues and possible solutions.

I. INTRODUCTION

Federated learning addresses growing privacy concerns by keeping data at end-user equipment while aggregating models centrally, but private information can still leak through attacks. The paper investigates these privacy and security issues, related design challenges, and possible solutions.

  • Privacy has become critical because centralized repositories and open datasets can expose health, travel, financial, and other personal information.Source: §I · p. 1
  • FL decouples data provision at end-user equipment from machine-learning model aggregation at a centralized server.Source: §I · p. 1
  • Even without sharing raw data, adversaries may reconstruct approximate data or infer private information from exposed parameter updates and gradients.Source: §I · p. 1
  • The paper investigates privacy and security protection methods and examines convergence, data poisoning, scaling up, and model aggregation.Source: §I · p. 2

II. BACKGROUND

The paper distinguishes security from privacy and introduces federated learning’s iterative client-server training process. Security concerns unauthorized access or disruption, whereas privacy concerns unintended disclosure of personal information.

  • Each FL client downloads a shared model, trains locally, uploads parameters, and receives the server’s aggregated model across communication rounds.Source: §II · p. 2
  • Security concerns unauthorized or malicious access, alteration, or denial of data, with confidentiality, integrity, and availability as its fundamental goals.Source: A. Difference between Security and… · p. 2
  • Privacy concerns unintentional disclosure of personal information, including identification from linking quasi-identifiers across datasets.Source: A. Difference between Security and… · p. 2

B. Security and Privacy Protection for FL

FL protection methods address privacy at clients or servers and security across the framework. The paper describes perturbation, dummy parameters, aggregation, and secure multiparty computation as privacy-oriented approaches.

Privacy protection at the client side

Privacy protection at the server side

3) Security protection for FL framework:

Security protection in FL addresses model-stealing threats, including hidden backdoors introduced into the joint global model. The paper notes that existing backdoor defenses face important data-access and auditing constraints.

III. CHALLENGES ON PRIVATE AND SECURE FL

Private and secure FL faces unresolved challenges spanning convergence, privacy–performance trade-offs, and protection against perturbation-induced optimization changes. The paper organizes these challenges and identifies theoretical, empirical, and algorithmic needs.

  • The paper examines four main issues in private and secure FL, including convergence, data poisoning, scaling up, and model aggregation.Source: §III · p. 3

Convergence

Research needs

  • Future work should establish convergence theory, evaluate accuracy and communication behavior, and design privacy algorithms while studying privacy–convergence-speed trade-offs.Source: A. Convergence: An Issue Caused by… · p. 3

B. Data Poisoning: A Security Issue

FL enables clients to contribute model updates, but malicious participants can exploit this flexibility to manipulate training and threaten system security. The section frames detection, performance assessment, and communication protection as open challenges.

Open questions

C. Scaling Up Issue: A Privacy and Security Issue

Scaling FL to many UEs introduces availability, connectivity, orchestration, storage, and computation constraints, while also leaving privacy and learning effects uncertain. Aggregation must therefore address heterogeneous clients and privacy-induced noise.

Model aggregation

IV. EXPERIMENT RESULTS AND POSSIBLE SOLUTIONS

Simulations examine how client-side noise affects federated-learning convergence and accuracy, while identifying a fundamental privacy–learning-performance tradeoff. CNN and MLP experiments illustrate architecture-dependent effects of perturbation.

Experimental setup

  • Experiments partition MNIST into disjoint non-i.i.d. client datasets, compute local SGD updates, and average them into a globally shared classifier.The prototype evaluates a 10-digit classification task.Source: §IV · p. 4

A. Convergence

  • Client-side Gaussian and Laplace perturbations with equal power are added to local parameters to provide privacy protection in the CNN system.The noise distributions are N1 ∼N(0, δ) and N2 ∼Lap(λ).Source: A. Convergence · p. 4
  • The MLP experiment finds that added noise has slight influence on accuracy, partly because auto-filtering removes poorly performing perceptions or parameters.This observation is presented in Fig. 3.Source: A. Convergence · p. 4, Figure 3 · p. 5

Possible solutions

  • Higher privacy protection requires more noise and leads to worse learning performance, creating a fundamental privacy–performance tradeoff.The paper identifies noise scale, local training iterations, communication rounds, and client count as variables requiring theoretical investigation.Source: A. Convergence · pp. 4–5

A. Convergence

  • The CNN experiments assess communication rounds versus accuracy under different noise powers.These comparisons are presented in Fig. 2.Source: Figure 2 · p. 5

B. Data Poisoning

The data-poisoning experiments examine malicious client updates in CNN federated learning and motivate defenses based on client recognition and aggregation mechanisms. Increasing malicious participation can degrade performance and eventually cause system failure.

B. Data Poisoning

  • In a 30-client CNN system, malicious clients upload fake parameters that are opposite to the true values or random numbers in [-1, 1].The attack occurs in each communication round.Source: B. Data Poisoning · p. 5
  • Malicious clients influence system performance, and the system fails when more malicious clients participate.The comparison is shown in Fig. 4.Source: B. Data Poisoning · p. 5, Figure 4 · p. 5
  • Data-poisoning prevention requires mechanisms that address malicious clients in privacy-aware federated learning.The paper identifies this as a technical problem.Source: B. Data Poisoning · p. 5

Possible solutions

  • One defense strategy recognizes malicious clients during setup or in each communication round using machine-learning techniques.The paper gives supervised learning as an example.Source: B. Data Poisoning · p. 5
  • Another defense strategy evaluates uploaded learning parameters during aggregation to identify or mitigate harmful client contributions.The passage introduces aggregation-focused defenses but does not specify the complete mechanism.Source: B. Data Poisoning · p. 5

C. Scaling Up Issue

Scaling the number of clients produces limited performance gains but can substantially reduce total delay under the paper’s delay model. The paper therefore considers deadlines and client clustering to address waiting and allocation challenges.

C. Scaling Up Issue

  • Increasing the number of clients does not produce much performance gain.The comparison is shown in Fig. 5.Source: C. Scaling Up Issue · pp. 5–6
  • More clients can largely reduce total delay when learning stops after accuracy exceeds 90%.Delay sums the maximum calculation and transmission time per communication round for clients randomly distributed in a 1×1 km2 area.Source: C. Scaling Up Issue · p. 6
  • The server may face long waiting times and complicated resource allocation during parameter uploading in large-scale networks.This is identified as the scaling-up technical problem.Source: C. Scaling Up Issue · p. 6

Possible solutions

  • An upload-delay deadline can let the server proceed after collecting the required client parameters or abandon an epoch when waiting exceeds the deadline.This approach targets long waiting time during federated-learning rounds.Source: C. Scaling Up Issue · p. 6
  • User clustering from game theory can partition many clients into groups to address large-scale client management.The passage presents clustering as a solution for the large number of clients.Source: C. Scaling Up Issue · p. 6

D. Model Aggregation

The paper proposes intelligent aggregation to address malicious clients while maintaining system performance. Its broader FL security discussion classifies challenges across convergence, data poisoning, scaling up, and model aggregation.

Model Aggregation

  • The proposed intelligent aggregation method addresses the malicious clients’ problem.It adds server-side testing and adjusts aggregation weights according to testing performance.Source: D. Model Aggregation · p. 6
  • Server-side testing updates aggregation weights based on the testing performance of each client’s uploaded parameters.Source: D. Model Aggregation · p. 6
  • Increasing local epochs can alleviate performance degradation caused by malicious clients, especially when more malicious clients exist.Source: D. Model Aggregation · p. 6
  • The paper identifies convergence, data poisoning, scaling up, and model aggregation as major FL privacy and security issues.Source: §V · p. 6

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