Traditional federated learning (FL) relies on a central aggregator server, which can create performance bottlenecks and privacy risks. Decentralized mix-and-forward designs remove the server, but repeated local mixing can attenuate global information under heterogeneity and expose peer-to-peer neighborhoods as a privacy attack surface. To preserve FedAvg-style aggregation semantics over updates reconstructable by the round deadline while scaling dissemination, we present FLTorrent, a BitTorrent-based dissemination layer for serverless FL with a short warm-up. Warm-up hardens within-round source unlinkability, a dissemination-layer goal orthogonal to content protections such as DP or secure aggregation, via pre-round obfuscation, randomized lags, and coordination-only non-owner-first scheduling with the tracker off the data path, before switching to vanilla BitTorrent swarming. We upper-bound the per-transfer attribution posterior by the fraction of owner chunks in a sender's eligible cover set, and derive a tighter high-probability bound that improves with early non-owner mass. A simple heuristic, GreedyFastestFirst, attains about 92% of a bandwidth-optimal max-flow upper bound, while warm-up remains a stable about 12% share of a round across 100-500 peers. Under an observation-only local adversary, FLTorrent drives attribution success close to neighborhood-level random guessing for typical nodes, improves with network size, and remains robust under collusion. In LLM-scale dissemination stress tests over 7-10 Gbps access links, FLTorrent adds only about 6-10% round-time overhead relative to BitTorrent-only. Overall, FLTorrent shows that within-round unlinkability and BitTorrent-level efficiency can co-exist with predictable, low overheads at scale.

翻译：传统联邦学习依赖中心化的聚合服务器，这会带来性能瓶颈和隐私风险。去中心化的混合-转发设计取消了服务器，但重复的局部混合会削弱异构性下的全局信息，并使对等网络邻域成为隐私攻击面。为了在保留FedAvg式聚合语义的前提下，使更新可按时重建并扩展传播规模，我们提出FLTorrent——一种基于BitTorrent的无服务器联邦学习传播层，具有短预热阶段。预热通过轮次前混淆、随机延迟以及协调性非所有者优先调度（追踪器不参与数据路径），实现了轮次内来源不可链接性——这是独立于差分隐私或安全聚合等内容保护的传播层目标——之后切换至标准BitTorrent群组模式。我们给出了单次传输后验归因的上界，其值等于发送方覆盖集中所有者块的比例，并推导出一个更紧的高概率上界，该上界随早期非所有者块数量的增加而改善。简单启发式算法GreedyFastestFirst能达到带宽最优最大流上界的约92%，而预热在100-500节点规模下仍稳定占用轮次时长的约12%。在仅观测的局部敌手攻击下，FLTorrent使典型节点的归因成功率接近邻域级随机猜测水平，且随网络规模扩大而改善，并在合谋攻击下保持鲁棒性。在7-10 Gbps接入链路的LLM规模传播压力测试中，FLTorrent相比纯BitTorrent仅增加约6-10%的轮次时间开销。总体而言，FLTorrent表明，轮次内不可链接性与BitTorrent级效率可在可预测的低开销下大规模共存。