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The security and performance of blockchain systems such as Bitcoin critically rely on the P2P network. This paper aims to investigate blockchain P2P networks. We explore the topologies, peer discovery, and data forwarding and examine the security and performance of the P2P network. Further, we formulate an optimization problem to study the theoretical limit of the performance and provide a solution to achieve optimal performance in a blockchain P2P network. 

Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central server. Consequently, secure aggregation protocols for FL have been developed to conceal the local models from the server. However, we show that, by manipulating the client selection process, the server can circumvent the secure aggregation to learn the local models of a victim client, indicating that secure aggregation alone is inadequate for privacy protection. To tackle this issue, we leverage blockchain technology to propose a verifiable client selection protocol. Owing to the immutability and transparency of blockchain, our proposed protocol enforces a random selection of clients, making the server unable to control the selection process at its discretion. We present security proofs showing that our protocol is secure against this attack. Additionally, we conduct several experiments on an Ethereum-like blockchain to demonstrate the feasibility and practicality of our solution.