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This paper studies information theoretic secure aggregation in federated learning, which involves K distributed nodes and a central server. For security, the server can only recover aggregated updates of locally trained models, without any other information about the local users’ data being leaked. The secure aggregation process typically consists of two phases: the key sharing phase and the model aggregation phase. In previous research, a constraint on keys known as “uncoded groupwise keys” was introduced, and we adopt this constraint during the key sharing phase, where each set of S -users shares an independent key. During the model aggregation phase, each user transmits its encrypted model results to the server. To tolerate user dropouts in secure aggregation (i.e., some users may not respond), where up to K−U users may drop out and the identity of the surviving users is unpredictable in advance, at least two rounds of transmission are required in the model aggregation phase. In the first round, users send the masked models. Then, in the second round, based on the identity of the surviving users after the first round, these surviving users send additional messages that assist the server in decrypting the sum of the users’ trained models. Our goal is to minimize the number of transmissions in the two rounds. Additionally, we consider the potential impact of user collusion, where up to T users may collude with the server. This requires the transmissions to meet stricter security constraints, ensuring that the server cannot learn anything beyond the aggregated model updates, even if it colludes with any set of up to T users. For this more challenging problem, we propose schemes that ensure secure aggregation and achieve the capacity region when S∈{2}∪[K−U+1:K−T] . Experimental results conducted on Tencent Cloud also show that the proposed secure aggregation schemes improve the model aggregation time compared to the benchmark scheme.
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The Capacity Region of Information Theoretic Secure Aggregation with Uncoded Groupwise Keys
This paper considers the secure aggregation problem for federated learning under an information theoretic cryptographic formulation, where distributed training nodes (referred to as users) train models based on their own local data and a curious-but-honest server aggregates the trained models without retrieving other information about users’ local data. Secure aggregation generally contains two phases, namely key sharing phase and model aggregation phase. Due to the common effect of user dropouts in federated learning, the model aggregation phase should contain two rounds, where in the first round the users transmit masked models and, in the second round, according to the identity of surviving users after the first round, these surviving users transmit some further messages to help the server decrypt the sum of users’ trained models. The objective of the considered information theoretic formulation is to characterize the capacity region of the communication rates from the users to the server in the two rounds of the model aggregation phase, assuming that key sharing has already been performed offline in prior. In this context, Zhao and Sun completely characterized the capacity region under the assumption that the keys can be arbitrary random variables. More recently, an additional constraint, known as “uncoded groupwise keys,” has been introduced. This constraint entails the presence of multiple independent keys within the system, with each key being shared by precisely S users, where S is a defined system parameter. The capacity region for the information theoretic secure aggregation problem with uncoded groupwise keys was established in our recent work subject to the condition S > K - U, where K is the number of total users and U is the designed minimum number of surviving users (which is another system parameter). In this paper we fully characterize the capacity region for this problem by matching a new converse bound and an achievable scheme. Experimental results over the Tencent Cloud show the improvement on the model aggregation time compared to the original secure aggregation scheme.
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- PAR ID:
- 10527831
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Information Theory
- ISSN:
- 0018-9448
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TIT.2024.3393740
