Kavli Affiliate: Xiang Zhang
| First 5 Authors: Xiang Zhang, Kai Wan, Hua Sun, Shiqiang Wang, Mingyue Ji
| Summary:
Secure aggregation is concerned with the task of securely uploading the
inputs of multiple users to an aggregation server without letting the server
know the inputs beyond their summation. It finds broad applications in
distributed machine learning paradigms such as federated learning (FL) where
multiple clients, each having access to a proprietary dataset, periodically
upload their locally trained models (abstracted as inputs) to a parameter
server which then generates an aggregate (e.g., averaged) model that is sent
back to the clients as an initializing point for a new round of local training.
To enhance the data privacy of the clients, secure aggregation protocols are
developed using techniques from cryptography to ensure that the server infers
no more information of the users’ inputs beyond the desired aggregated input,
even if the server can collude with some users. Although laying the ground for
understanding the fundamental utility-security trade-off in secure aggregation,
the simple star client-server architecture cannot capture more complex network
architectures used in practical systems. Motivated by hierarchical federated
learning, we investigate the secure aggregation problem in a $3$-layer
hierarchical network consisting of clustered users connecting to an aggregation
server through an intermediate layer of relays. Besides the conventional server
security which requires that the server learns nothing beyond the desired sum
of inputs, relay security is also imposed so that the relays infer nothing
about the users’ inputs and remain oblivious. For such a hierarchical secure
aggregation (HSA) problem, we characterize the optimal multifaceted trade-off
between communication (in terms of user-to-relay and relay-to-server
communication rates) and secret key generation efficiency (in terms of
individual key and source key rates).
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