Peer-to-Peer Federated Learning: A Comprehensive Survey

The last five years have seen considerable growth in the topic of peer-to-peer (P2P) federated learning (FL). This framework removes the central coordinating server used in conventional federated learning and instead requires participating nodes to manage model training, peer selection, communication, aggregation, and trust directly. This provides a promising route for privacy-preserving and decentralised machine learning, but it also introduces unresolved challenges in topology selection, participant incentivisation, communication efficiency, security, and evaluation. Existing studies frequently evaluate proposed methods under narrow assumptions, such as static network membership, homogeneous devices, fixed bandwidth, limited topology choices, and public benchmark datasets. Existing surveys also tend to present taxonomies of decentralised federated learning rather than synthesising how topology, incentives, and communication algorithms jointly affect deployability. This paper reviews recent work on peer-to-peer federated learning across three connected dimensions: network topology, incentive mechanisms, and communication algorithms. We compare the topologies, datasets, experimental assumptions, incentive designs, communication strategies, and open issues reported in the literature. The review shows that highly connected topologies tend to improve convergence but increase communication overhead and vulnerability to bottlenecks; sparse and dynamic topologies improve efficiency but create challenges for convergence, reliability, and node drop-out. Incentive mechanisms increasingly combine reward, reputation, validation, and punishment but remain weakly validated under realistic churn, heterogeneous resources, and adversarial behaviour. Communication algorithms reduce bandwidth through gossip, sparsification, prediction, routing, and multi-step aggregation but often trade communication savings against accuracy, robustness, and generalisability. Across all three areas, the field lacks standardised benchmarks, reproducible experimental settings, and realistic evaluation under unstable peer-to-peer conditions. We conclude by identifying cross-cutting research gaps and recommending future work on dynamic topologies, heterogeneous devices, real-world datasets, incentive robustness, and comparable benchmarking.