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Flatee: Federated Learning Across Trusted Execution Environments
Cryptography and Security · Submitted 12 Nov 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We describe Flatee, an efficient privacy-preserving federated learning framework across TEEs, which considerably reduces training and communication time.
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The abstract, sentence by sentence
Federated learning allows us to distributively train a machine learning model where multiple parties share local model parameters without sharing private data. However, parameter exchange may still leak information. Several approaches have been proposed to overcome this, based on multi-party computation, fully homomorphic encryption, etc.; many of these protocols are slow and impractical for real-world use as they involve a large number of cryptographic operations. In this paper, we propose the use of Trusted Execution Environments (TEE), which provide a platform for isolated execution of code and handling of data, for this purpose. We describe Flatee, an efficient privacy-preserving federated learning framework across TEEs, which considerably reduces training and communication time. Our framework can handle malicious parties (we do not natively solve adversarial data poisoning, though we describe a preliminary approach to handle this).
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Federated learning allows us to distributively train a machine learning model where multiple parties share local model parameters without sharing private data. | background 1.00 | 0.05 | 0.03 | asserted 0.98 |
| s1 | However, parameter exchange may still leak information. | background 0.84 | 0.07 | 0.02 | hedged possibility 0.99 |
| s2 | Several approaches have been proposed to overcome this, based on multi-party computation, fully homomorphic encryption, etc.; many of these protocols are slow and impractical for real-world use as they involve a large number of cryptographic operations. | background 0.83 | 0.05 | 0.03 | asserted 0.99 |
| s3 | In this paper, we propose the use of Trusted Execution Environments (TEE), which provide a platform for isolated execution of code and handling of data, for this purpose. | contribution 0.76 | 0.39 | 0.10 | asserted 0.96 |
| s4 | We describe Flatee, an efficient privacy-preserving federated learning framework across TEEs, which considerably reduces training and communication time. | contribution 0.97 | 0.91 | 0.74 | asserted 1.00 |
| s5 | Our framework can handle malicious parties (we do not natively solve adversarial data poisoning, though we describe a preliminary approach to handle this). | limitation 0.92 | 0.87 | 0.06 | asserted 0.97 |
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The paper
Original abstract
Federated learning allows us to distributively train a machine learning model where multiple parties share local model parameters without sharing private data. However, parameter exchange may still leak information. Several approaches have been proposed to overcome this, based on multi-party computation, fully homomorphic encryption, etc.; many of these protocols are slow and impractical for real-world use as they involve a large number of cryptographic operations. In this paper, we propose the use of Trusted Execution Environments (TEE), which provide a platform for isolated execution of code and handling of data, for this purpose. We describe Flatee, an efficient privacy-preserving federated learning framework across TEEs, which considerably reduces training and communication time. Our framework can handle malicious parties (we do not natively solve adversarial data poisoning, though we describe a preliminary approach to handle this).
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How the literature uses it
5 arXiv papers cite this in 5 sentences
Papers citing it · 1 sentence without a confident label
Cites it as background 4 papers
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Several approaches have been proposed to overcome this leakage problem based on differential privacy (DP) [ref] , MPC [ref] , HE [ref] , Trusted Execution Environment [ref] , etc.
SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation · Related Work · 0.93
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To overcome this leakage problem, several approaches have been proposed based on differential privacy [ref] , multi-party computation [ref] , fully homomorphic encryption [ref] , Trusted Execution Environment [ref] , etc.
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning · Related Work · 0.88
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Systems such as ShuffleFL [ref] , Flatee [ref] , and Papaya [ref] use secure hardware for private averaging to prevent the server from inspecting model updates.
Noiseless Privacy-Preserving Decentralized Learning · Related Work · 0.94
What it cites
Uses the method or tool of 1 paper
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$𝒮$ and all $𝒫$ have SGX-enabled machines, and agree to train a model $ℳ_FL$. $𝒮$ uses its SGX module to authenticate all $𝒫$ and aggregate the parties' trained models using the federated average function [ref] (e.g., weighted mean, geometric median etc.) to generate the global trained model.
Communication-Efficient Learning of Deep Networks from Decentralized Data · Setup. · 0.98
Cites as background 2 papers
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Federated learning (FL) [ref] is a distributed approach to Machine Learning which allows models to be trained on a large body of decentralized data with many participants.
Communication-Efficient Learning of Deep Networks from Decentralized Data · Federated Learning · 1.00
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Multi-party computation has been used for privacy-preserving ML [ref] .
S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training · Introduction · 0.94
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