Scale Wisely, Secure Wholly: P2P Swarm Learning Over Consortium Blockchain in Edge Networks

Swarm Learning (SL) provides a secure distributed learning environment to edge computing (EC) networks by leveraging blockchain technology for certified participation, encrypted information transmission, and immutable data storage. However, vanilla SL faces scalability limitations due to system-wide model aggregation, which bottlenecks its communication and blockchain efficiency. This paper presents a novel framework called Peer-to-peer Swarm Learning Over consOrtium blOckchain
$(\mathbf{PSLO}_{3})$
to enhance the scalability of SL over EC networks. PSLO
3
proposes a peer-to-peer swarm learning (P2P-SL) mechanism that only requires local communications for P2P model aggregation, thereby reducing the communication overhead of vanilla SL for system-wide model aggregation. Furthermore, PSLO
3
delivers P2P-SL over consortium blockchain and strategically organizes the edge servers into subchains to minimize the overhead of P2P-SL over consortium blockchain. A subchain formation scheme is designed based on graph partitioning by jointly analyzing the topological property of EC networks, message-passing patterns of P2P-SL, and the overhead of cross-/intra-chain interactions. An evaluation environment is built based on the Wecross platform to evaluate the performance of PSLO
3
. Experimental results demonstrate that PSLO
3
provides a reduction of 81.1% in communication overhead and a reduction of 26.03% in blockchain cost compared to vanilla swarm learning while demonstrating comparable learning performances.