Privacy-preserving Chunk Scheduling in a BitTorrent Implementation of Federated Learning

Abstract

Traditional federated learning (FL) relies on a central aggregator server, which can create performance bottlenecks and privacy risks. Decentralized \emph{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 \textbf{\emph{FLTorrent}}, a BitTorrent-based dissemination layer for serverless FL with a short warm-up. Warm-up hardens \emph{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, \textsc{GreedyFastestFirst}, attains $\approx 92\%$ of a bandwidth-optimal max-flow upper bound, while warm-up remains a stable $\approx 12\%$ 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 $7$--$10\,\mathrm{Gbps}$ access links, FLTorrent adds only $\sim 6$--$10\%$ 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.