Summary Fully Homomorphic Encryption for Privacy-Preserving Federated Learning arxiv.org
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One Line
FheFL utilizes fully homomorphic encryption to secure model updates and safeguard private information in federated learning, surpassing other aggregation methods in terms of resilience against data poisoning.
Slides
Slide Presentation (9 slides)
Key Points
- The FheFL algorithm is a novel approach that addresses privacy and poisoning attacks in federated learning (FL).
- FheFL utilizes fully homomorphic encryption (FHE) to protect model updates and prevent the server from inferring private information.
- The algorithm incorporates a non-poisoning rate-based aggregation scheme to effectively address data poisoning attacks.
- FheFL only requires one server for secure aggregation and introduces a recursive weighted update scheme to detect and mitigate the impact of poisoning users without user interaction.
- The CKKS FHE scheme is used in FheFL, which supports approximate homomorphic computations over vectors with real numbers and ensures security.
- The algorithm utilizes a pairwise secret sharing scheme for encryption and decryption in the distributed FL setting.
- FheFL demonstrates comparable accuracy, robustness against data poisoning attacks, and reasonable computational and communication complexity.
Summaries
30 word summary
FheFL uses fully homomorphic encryption to protect model updates and prevent access to private information in federated learning. It outperforms other aggregation schemes in terms of robustness against data poisoning.
69 word summary
FheFL is a new algorithm that uses fully homomorphic encryption (FHE) to protect model updates and prevent access to private information in federated learning (FL). It incorporates a distributed multi-key additive homomorphic encryption scheme for model aggregation and introduces an encrypted domain aggregation scheme to address data poisoning attacks. FheFL is based on the CKKS FHE scheme and outperforms other aggregation schemes in terms of robustness against data poisoning.
177 word summary
The paper introduces FheFL, a new algorithm that tackles privacy and poisoning attacks in federated learning (FL). FheFL utilizes fully homomorphic encryption (FHE) to safeguard model updates and prevent the server from accessing private information. It incorporates a distributed multi-key additive homomorphic encryption scheme for model aggregation and introduces an encrypted domain aggregation scheme to effectively address data poisoning attacks. Unlike other approaches, FheFL uses only one server for secure aggregation and employs a recursive weighted update scheme to detect and mitigate the impact of poisoning users without user interaction. The algorithm is based on the CKKS FHE scheme, which supports homomorphic computations over vectors with real numbers. FheFL also incorporates a non-poisoning rate-based aggregation scheme that scales down a user's contribution to the global model based on their non-poisoning rate, minimizing the impact of poisoning attacks while considering legitimate updates. The algorithm utilizes a pairwise secret sharing scheme for encryption and decryption in the distributed FL setting. FheFL ensures privacy, security, and accuracy in FL, outperforming other aggregation schemes in terms of robustness against data poisoning.
380 word summary
The paper presents a new algorithm called FheFL that addresses privacy and poisoning attacks in federated learning (FL). FL is a collaborative learning scheme where users send their local gradients to a centralized server for model aggregation. The proposed FheFL algorithm uses fully homomorphic encryption (FHE) to protect model updates and prevent the server from inferring private information. It introduces a distributed multi-key additive homomorphic encryption scheme that supports model aggregation in FL. It also incorporates a novel aggregation scheme within the encrypted domain to effectively address data poisoning attacks. FheFL ensures both privacy and security in FL while achieving comparable accuracy at a reasonable computational cost.
FheFL is compared with existing works that focus on security and privacy in FL. In contrast to other approaches, FheFL utilizes only one server for secure aggregation and introduces a recursive weighted update scheme to detect and mitigate the impact of poisoning users without user interaction. The proposed scheme is robust, secure, and private, addressing both privacy and security concerns in FL.
The algorithm is based on the CKKS FHE scheme, which supports approximate homomorphic computations over vectors with real numbers. It uses lattice-based techniques and the Learning With Error (LWE) problem to ensure security. The scheme supports homomorphic addition and multiplication in the encrypted domain, allowing for computation on encrypted data.
The FheFL algorithm incorporates a non-poisoning rate-based aggregation scheme, where the server calculates the squared Euclidean distance between the global model and each user's model update. The distance is used to determine the user's non-poisoning rate, scaling down their contribution to the global model. This approach minimizes the impact of poisoning attacks while considering legitimate updates.
To enable encryption and decryption in the distributed FL setting, the algorithm utilizes a pairwise secret sharing scheme. Each user shares a secret key with other users, and the server aggregates the encrypted model updates using the shared secret keys.
Overall, FheFL is a novel FL algorithm that addresses privacy and security concerns by leveraging fully homomorphic encryption and a non-poisoning rate-based aggregation scheme. It eliminates privacy leakage, protects against data poisoning attacks, and ensures user data remains secure throughout the learning process. Experimental analysis demonstrates that the proposed FheFL scheme outperforms other popular aggregation schemes in terms of accuracy and robustness against data poisoning.
653 word summary
The paper introduces a novel algorithm called FheFL that addresses privacy and poisoning attacks in federated learning (FL). FL is a collaborative learning scheme where users send their local gradients to a centralized server for model aggregation. However, sharing gradients can lead to privacy leakage and malicious users can launch poisoning attacks by sending fake updates. Existing techniques like secure aggregation and differential privacy have limitations in terms of security and accuracy.
To mitigate both attacks, the proposed FheFL algorithm utilizes fully homomorphic encryption (FHE) to protect the model updates and prevent the server from inferring private information. It introduces a distributed multi-key additive homomorphic encryption scheme that supports model aggregation in FL. It also incorporates a novel aggregation scheme within the encrypted domain, considering users' non-poisoning rates, to effectively address data poisoning attacks. The approach ensures both privacy and security in FL while achieving comparable accuracy at a reasonable computational cost.
FheFL is compared with existing works that focus on security and privacy in FL. Some rely on two non-colluding servers or collaboration between users, while others follow an all-or-nothing approach. In contrast, FheFL utilizes only one server for secure aggregation and introduces a recursive weighted update scheme to detect and mitigate the impact of poisoning users without user interaction. The proposed scheme is robust, secure, and private, addressing both privacy and security concerns in FL.
The algorithm is based on the CKKS FHE scheme, supporting approximate homomorphic computations over vectors with real numbers. CKKS is efficient and suitable for applications relying on vectors of real numbers. The scheme uses lattice-based techniques and the Learning With Error (LWE) problem to ensure security. It supports homomorphic addition and multiplication in the encrypted domain, allowing for computation on encrypted data.
The FheFL algorithm incorporates a non-poisoning rate-based aggregation scheme, where the server calculates the squared Euclidean distance between the global model and each user's model update. The distance is used to determine the user's non-poisoning rate, scaling down their contribution to the global model. This approach minimizes the impact of poisoning attacks while considering legitimate updates.
To enable encryption and decryption in the distributed FL setting, the algorithm utilizes a pairwise secret sharing scheme. Each user shares a secret key with other users, and the server aggregates the encrypted model updates using the shared secret keys. The server can reconstruct the secret key and decrypt the aggregated model. The key sharing process only needs to be executed once when a user joins the network.
Overall, FheFL is a novel FL algorithm that addresses privacy and security concerns by leveraging fully homomorphic encryption and a non-poisoning rate-based aggregation scheme. It eliminates privacy leakage, protects against data poisoning attacks, and ensures user data remains secure throughout the learning process.
The proposed FheFL scheme leverages Fully Homomorphic Encryption (FHE) to address privacy and security challenges in federated learning. It introduces a distributed multi-key additive HE scheme for secure model aggregation and a non-poisoning rate-based aggregation scheme to mitigate data poisoning attacks in the encrypted domain.
The FheFL scheme ensures privacy by encrypting model updates using the CKKS FHE scheme. The server performs computations on the encrypted data, allowing for secure aggregation. The server calculates the Euclidean distance between the user's model update and the previous global model in the encrypted domain.
To compute the global model, the server aggregates the encrypted model updates from all users using the multi-key HE scheme. The aggregated model is decrypted using the shared secret keys. The non-poisoning rate-based aggregation scheme minimizes the influence of malicious users by smoothing out gradients far from the intended direction.
The security of the proposed multi-key HE scheme is equivalent to that of the FHE schemes. The proposed scheme protects the privacy of individual user models if there are at least two non-colluding users.
Experimental analysis demonstrates that the proposed FheFL scheme outperforms other popular aggregation schemes in terms of accuracy and robustness against data poisoning
915 word summary
The paper proposes a novel algorithm called FheFL that addresses privacy and poisoning attacks in federated learning (FL). FL is a collaborative learning scheme where users hold a portion of the distributed training data and send their local gradients to a centralized server for model aggregation. However, sharing gradients can lead to privacy leakage and malicious users can launch poisoning attacks by sending fake updates. Existing techniques like secure aggregation and differential privacy have drawbacks in terms of security and accuracy.
To mitigate both attacks, the proposed FheFL algorithm utilizes fully homomorphic encryption (FHE) to protect the model updates and prevent the server from inferring private information. The algorithm introduces a distributed multi-key additive homomorphic encryption scheme that supports model aggregation in FL. It also incorporates a novel aggregation scheme within the encrypted domain, taking into account users' non-poisoning rates, to effectively address data poisoning attacks. The approach ensures both privacy and security in FL while achieving comparable accuracy at a reasonable computational cost.
The paper compares FheFL with existing works that focus on security and privacy in FL. Some existing solutions rely on two non-colluding servers or collaboration between users, while others follow an all-or-nothing approach. In contrast, FheFL utilizes only one server for secure aggregation and introduces a recursive weighted update scheme to detect and mitigate the impact of poisoning users without user interaction. The proposed scheme is robust, secure, and private, addressing both privacy and security concerns in FL.
The algorithm is based on the CKKS FHE scheme, which supports approximate homomorphic computations over vectors with real numbers. CKKS is efficient and suitable for applications relying on vectors of real numbers. The scheme uses lattice-based techniques and the Learning With Error (LWE) problem to ensure security. It supports homomorphic addition and multiplication in the encrypted domain, allowing for computation on encrypted data.
The FheFL algorithm incorporates a non-poisoning rate-based aggregation scheme, where the server calculates the squared Euclidean distance between the global model and each user's model update. The distance is used to determine the user's non-poisoning rate, which scales down their contribution to the global model. This approach minimizes the impact of poisoning attacks while still considering legitimate updates.
To enable encryption and decryption in the distributed FL setting, the algorithm utilizes a pairwise secret sharing scheme. Each user shares a secret key with other users, and the server aggregates the encrypted model updates using the shared secret keys. The server can reconstruct the secret key and decrypt the aggregated model. The key sharing process only needs to be executed once when a user joins the network.
Overall, FheFL is a novel FL algorithm that addresses privacy and security concerns by leveraging fully homomorphic encryption and a non-poisoning rate-based aggregation scheme. The algorithm is secure, private, and achieves comparable accuracy in FL. It eliminates privacy leakage, protects against data poisoning attacks, and ensures user data remains secure throughout the learning process.
The proposed FheFL scheme addresses privacy and security challenges in federated learning by leveraging Fully Homomorphic Encryption (FHE). FHE allows computations to be performed on encrypted data, ensuring the privacy of user data. The scheme introduces a distributed multi-key additive HE scheme for secure model aggregation, protecting individual user data during the aggregation process. Additionally, a novel non-poisoning rate-based aggregation scheme mitigates data poisoning attacks in the encrypted domain.
The FheFL scheme ensures privacy by encrypting model updates using the CKKS FHE scheme. The server can perform computations on the encrypted data, allowing for secure aggregation. The server calculates the Euclidean distance between the user's model update and the previous global model in the encrypted domain. This operation can be parallelized as it does not depend on other users' input.
To compute the global model, the server aggregates the encrypted model updates from all users using the multi-key HE scheme. The aggregated model is then decrypted using the shared secret keys. The non-poisoning rate-based aggregation scheme minimizes the influence of malicious users by smoothing out gradients far from the intended direction. The non-poisoning rate for malicious users decreases with each iteration.
The security of the proposed multi-key HE scheme is equivalent to that of the FHE schemes. If an adversary can break the multi-key HE scheme, they can also break the FHE schemes easily. The proposed scheme protects the privacy of individual user models if there are at least two non-colluding users.
The convergence analysis shows that if the number of benign users is higher than the number of malicious users, the proposed non-poisoning rate-based weighted aggregation converges to a model that is the same as the benign users' only model. The training loss decreases with each epoch when the percentage of attackers is small, supporting the convergence analysis.
Experimental analysis demonstrates that the proposed FheFL scheme outperforms other popular aggregation schemes in terms of accuracy and robustness against data poisoning attacks. The computational complexity analysis shows that the proposed scheme is efficient, with computational time comparable to other state-of-the-art schemes. The communication complexity analysis shows that the bandwidth requirement for communication between users and the server is reasonable.
In conclusion, the FheFL scheme offers a significant advancement in privacy-preserving federated learning. It ensures privacy and security through the use of FHE and a multi-key HE scheme. The non-poisoning rate-based aggregation scheme mitigates data poisoning attacks. The scheme demonstrates comparable accuracy with reasonable computational costs and robust security guarantees. It eliminates the need for user interaction in each epoch and sets the stage for future advancements in privacy-preserving federated learning.