Collusion-Resilient Hierarchical Secure Aggregation with Heterogeneous Security Constraints
- URL: http://arxiv.org/abs/2507.14768v1
- Date: Sat, 19 Jul 2025 23:09:57 GMT
- Title: Collusion-Resilient Hierarchical Secure Aggregation with Heterogeneous Security Constraints
- Authors: Zhou Li, Xiang Zhang, Jiawen Lv, Jihao Fan, Haiqiang Chen, Giuseppe Caire,
- Abstract summary: Motivated by federated learning (FL), secure aggregation aims to securely compute, as efficiently as possible, the sum of a set of inputs distributed across many users.<n>We study weakly-secure HSA (WS-HSA) with collusion resilience.<n>We characterize the optimal total key rate, i.e., the total number of independent key symbols required to ensure both server and relay security.
- Score: 42.80769898523078
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Motivated by federated learning (FL), secure aggregation (SA) aims to securely compute, as efficiently as possible, the sum of a set of inputs distributed across many users. To understand the impact of network topology, hierarchical secure aggregation (HSA) investigated the communication and secret key generation efficiency in a 3-layer relay network, where clusters of users are connected to the aggregation server through an intermediate layer of relays. Due to the pre-aggregation of the messages at the relays, HSA reduces the communication burden on the relay-to-server links and is able to support a large number of users. However, as the number of users increases, a practical challenge arises from heterogeneous security requirements--for example, users in different clusters may require varying levels of input protection. Motivated by this, we study weakly-secure HSA (WS-HSA) with collusion resilience, where instead of protecting all the inputs from any set of colluding users, only the inputs belonging to a predefined collection of user groups (referred to as security input sets) need to be protected against another predefined collection of user groups (referred to as collusion sets). Since the security input sets and collusion sets can be arbitrarily defined, our formulation offers a flexible framework for addressing heterogeneous security requirements in HSA. We characterize the optimal total key rate, i.e., the total number of independent key symbols required to ensure both server and relay security, for a broad range of parameter configurations. For the remaining cases, we establish lower and upper bounds on the optimal key rate, providing constant-factor gap optimality guarantees.
Related papers
- Edge-Assisted Collaborative Fine-Tuning for Multi-User Personalized Artificial Intelligence Generated Content (AIGC) [38.59865959433328]
Cloud-based solutions aid in computation but often fall short in addressing privacy risks, personalization efficiency, and communication costs.<n>We propose a novel cluster-aware hierarchical federated aggregation framework.<n>We show that the framework achieves accelerated convergence while maintaining practical viability for scalable multi-user personalized AIGC services.
arXiv Detail & Related papers (2025-08-06T06:07:24Z) - Information-Theoretic Decentralized Secure Aggregation with Collusion Resilience [98.31540557973179]
We study the problem of decentralized secure aggregation (DSA) from an information-theoretic perspective.<n>We characterize the optimal rate region, which specifies the minimum achievable communication and secret key rates for DSA.<n>Our results establish the fundamental performance limits of DSA, providing insights for the design of provably secure and communication-efficient protocols.
arXiv Detail & Related papers (2025-08-01T12:51:37Z) - Practical Secure Aggregation by Combining Cryptography and Trusted Execution Environments [1.3068730884406587]
Secure aggregation enables a group of mutually distrustful parties, each holding private inputs, to collaboratively compute an aggregate value.<n>A major challenge in adopting secure aggregation approaches for practical applications is the significant computational overhead of the underlying cryptographic protocols.<n> Hardware-based security techniques such as trusted execution environments (TEEs) enable computation at near-native speeds.<n>In this work, we introduce several secure aggregation architectures that integrate both cryptographic and TEE-based techniques.
arXiv Detail & Related papers (2025-04-11T07:49:09Z) - Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association [93.46811590752814]
Hierarchical secure aggregation is motivated by federated learning (FL)<n>In this paper, we consider HSA with a cyclic association pattern where each user is connected to $B$ consecutive relays.<n>We propose an efficient aggregation scheme which includes a message design for the inputs inspired by gradient coding.
arXiv Detail & Related papers (2025-03-06T15:53:37Z) - Co-clustering for Federated Recommender System [33.70723179405055]
Federated Recommender System (FRS) offers a solution that strikes a balance between providing high-quality recommendations and preserving user privacy.
The presence of statistical heterogeneity in FRS, commonly observed due to personalized decision-making patterns, can pose challenges.
We propose CoFedRec, a novel Co-clustering Federated Recommendation mechanism.
arXiv Detail & Related papers (2024-11-03T21:32:07Z) - PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning [49.916365792036636]
Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data.<n>The transmitted model updates can potentially leak sensitive user information, and the lack of central control of the local training process leaves the global model susceptible to malicious manipulations on model updates.<n>We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy.
arXiv Detail & Related papers (2024-07-12T03:18:08Z) - Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive
Privacy Analysis and Beyond [57.10914865054868]
We consider vertical logistic regression (VLR) trained with mini-batch descent gradient.
We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks.
arXiv Detail & Related papers (2022-07-19T05:47:30Z) - Safe RAN control: A Symbolic Reinforcement Learning Approach [62.997667081978825]
We present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications.
We provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology.
We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions.
arXiv Detail & Related papers (2021-06-03T16:45:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.