Asynchronous Cache-based Aggregation with Fairness and Filtering for Decentralized Federated Learning

Decentralized Federated Learning (DFL) offers a scalable paradigm for collaborative intelligence at the edge, yet its practical efficacy is severely constrained by system heterogeneity. Traditional synchronous protocols enforce rigid, lockstep aggregation barriers, where the training velocity of the entire collective is strictly dictated by the slowest straggler node, inevitably leading to significant idle time and resource underutilization. While asynchronous strategies mitigate latency, they often introduce complex pathologies, such as unbounded staleness and systemic unfairness, because high-performance nodes disproportionately bias the global model toward local data distributions, thereby marginalizing slower contributors. To rigorously reconcile these conflicting trade-offs, this work presents CAFF, a novel asynchronous communication framework for DFL that decouples local optimization from global synchronization via a topology-aware, event-driven protocol. By implementing a topology-aware cache with a strict per-neighbor replacement policy, the mechanism limits per-peer dominance by enforcing a one-slot-per-neighbor cache and exclusive replacement, preventing any peer from contributing multiple updates within a single aggregation event. Furthermore, a configurable staleness filter and a dynamic aggregation threshold ensure robust convergence stability across diverse federation topologies. Extensive empirical evaluations using MNIST, FashionMNIST, CIFAR-10, and SVHN, conducted on a high-fidelity, virtualized testbed across fully connected, star, and ring topologies, demonstrate that CAFF significantly outperforms synchronous baselines. Specifically, in dense network configurations, the framework reduces wall-clock training time by up to 39% and network traffic by up to 75%, while maintaining competitive predictive fidelity with controlled accuracy degradation. These results position CAFF as a robust and scalable efficiency-oriented solution for heterogeneous peer-to-peer learning environments.