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 exposes 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 (e.g., DP or secure aggregation) -- via (i) pre-round obfuscation, (ii) randomized lags, and (iii) coordination-only non-owner-first scheduling (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 approximately 92% of a bandwidth-optimal max-flow upper bound, while warm-up remains a stable approximately 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 stress tests (Gemma-7B, DeepSeek-R1-14B, Qwen2.5-32B, and Llama-3.3-70B) over 7--10 Gbps access links, FLTorrent adds only approximately 6--10% end-to-end 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（FL）依赖于中心聚合服务器，这可能造成性能瓶颈和隐私风险。去中心化的混合-转发设计移除了服务器，但重复的本地混合会在异质性下削弱全局信息，并将对等网络邻域暴露为隐私攻击面。为在扩展分发的同时保持FedAvg风格的聚合语义（即由轮次截止时间可重建的更新），我们提出FLTorrent——一种基于BitTorrent且具有短预热期的无服务器FL分发层。预热期通过以下机制强化轮内源不可链接性（一种与内容保护（如差分隐私或安全聚合）正交的分发层目标）：(i) 轮次前混淆、(ii) 随机延迟、(iii) 仅基于协调的非所有者优先调度（追踪器脱离数据路径），之后切换为普通BitTorrent群集。我们将每次传输的归因后验概率上界设定为发送者合格覆盖集合中所有者分块的比例，并推导出一个更紧的高概率上界，该上界随早期非所有者分块数量增加而改善。简单启发式算法GreedyFastestFirst可达到带宽最优最大流上界的约92%，而预热期在100-500个对等节点间稳定占每轮约12%的份额。在仅基于观测的本地敌手模型下，FLTorrent使典型节点的归因成功率接近邻域级随机猜测水平，其性能随网络规模增大而提升，且在合谋攻击下保持鲁棒性。在LLM规模的压力测试（Gemma-7B、DeepSeek-R1-14B、Qwen2.5-32B及Llama-3.3-70B，通过7-10 Gbps接入链路进行）中，FLTorrent相对于纯BitTorrent仅增加约6-10%的端到端开销。总体而言，FLTorrent表明轮内不可链接性与BitTorrent级效率可以在大规模场景下以可预测的低开销共存。