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 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.

This paper has been accepted to the 46th IEEE International Conference on Distributed Computing Systems (ICDCS 2026). Please cite the IEEE proceedings version once it becomes available