arxiv: 2604.10859 · v1 · submitted 2026-04-12 · 💻 cs.DC

Recognition: unknown

Understanding Communication Backends in Cross-Silo Federated Learning

Amir Ziashahabi , Chaoyang He , Salman Avestimehr

Authors on Pith no claims yet

Pith reviewed 2026-05-10 14:57 UTC · model grok-4.3

classification 💻 cs.DC

keywords federated learningcross-silocommunication backendsgRPChybrid backendperformance benchmarkinggeo-distributedlarge models

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The pith

A hybrid gRPC+S3 backend achieves up to 3.8 times faster end-to-end performance than gRPC for transmitting large models in geo-distributed cross-silo federated learning.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper benchmarks communication backends including MPI, gRPC, and PyTorch RPC for cross-silo federated learning and introduces a hybrid gRPC+S3 approach to address their limits with large models. It shows that the hybrid method overcomes latency problems in geo-distributed deployments by routing bulk data through object storage while keeping coordination via gRPC. A sympathetic reader would care because inefficient data movement can become a major bottleneck as model sizes increase and servers spread across locations. The benchmarks under realistic network conditions give concrete data for choosing backends based on model size and setup.

Core claim

The paper establishes that gRPC+S3, a hybrid backend designed to overcome the limitations of existing approaches, particularly for transmitting large models across geo-distributed deployments, achieves up to 3.8× end-to-end speedup over gRPC. Its benchmarks examine point-to-point and end-to-end performance for a broad range of model sizes running under realistic network conditions, providing practical insights for selecting and configuring suitable communication backends tailored to specific federated learning tasks and network configurations.

What carries the argument

The gRPC+S3 hybrid backend, which uses gRPC for control and coordination while offloading large model parameter transfers to S3 object storage.

Load-bearing premise

The tested network conditions, model sizes, and geo-distributed setups are representative of production cross-silo federated learning workloads.

What would settle it

Running identical benchmarks on real production geo-distributed clusters with models larger than those tested and finding no speedup or a slowdown for gRPC+S3 would falsify the central performance claim.

Figures

Figures reproduced from arXiv: 2604.10859 by Amir Ziashahabi, Chaoyang He, Salman Avestimehr.

**Figure 2.** Figure 2: Effect of concurrent dispatch on gRPC: band [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: Architecture of the proposed gRPC+S3 backend. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

**Figure 4.** Figure 4: Peer-to-peer results across backends, environments, and model sizes. (a) CPU-to-CPU latency (log scale). (b) Speedup of [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

**Figure 5.** Figure 5: Per-state duration in end-to-end LAN experiments (communication, CPU-GPU migration, serialization, waiting; plus [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗

read the original abstract

Federated learning (FL) has emerged as a practical means for privacy-preserving distributed machine learning. FL's versatile design makes it suitable for various training settings, from IoT edge devices in cross-device FL to powerful servers in cross-silo FL. A key consequence of this versatility is the high level of diversity found in the networking configuration of FL applications. Coupled with the rising demand for large-scale models such as large language models, well-informed selection and configuration of communication backends become crucial for ensuring optimal performance in FL systems. This work focuses on cross-silo federated learning, presenting in-depth benchmarks of various communication backends, including MPI, gRPC, and PyTorch RPC. In addition, we introduce gRPC+S3, a hybrid backend designed to overcome the limitations of existing approaches, particularly for transmitting large models across geo-distributed deployments, achieving up to $3.8\times$ end-to-end speedup over gRPC. Our benchmarks examine point-to-point and end-to-end performance for a broad range of model sizes running under realistic network conditions. Our findings provide practical insights for selecting and configuring suitable communication backends tailored to the specific federated learning tasks and network configurations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This is a straightforward empirical benchmark of communication backends for cross-silo FL, with a gRPC+S3 hybrid that reports up to 3.8x speedup for large models under the tested conditions.

read the letter

The paper's core contribution is a set of point-to-point and end-to-end benchmarks comparing MPI, gRPC, and PyTorch RPC in cross-silo federated learning, plus a hybrid gRPC+S3 design that routes large model updates through S3 to improve geo-distributed performance. The 3.8x end-to-end claim comes from running these across a range of model sizes under simulated network conditions. That gives practitioners some concrete numbers to work with when choosing a backend for big models spread across silos.

Referee Report

2 major / 2 minor

Summary. The paper benchmarks communication backends (MPI, gRPC, PyTorch RPC) for cross-silo federated learning and introduces a hybrid gRPC+S3 backend. It reports up to 3.8× end-to-end speedup for transmitting large models in geo-distributed settings, based on point-to-point and end-to-end measurements across model sizes under realistic network conditions, and provides practical guidance for backend selection in FL workloads.

Significance. If the results are representative of production conditions, the work supplies concrete performance data and a hybrid design that addresses a practical gap in scaling cross-silo FL to large models. The empirical comparisons can inform system builders facing diverse network topologies, though the value hinges on how closely the tested latencies, bandwidths, and geo-distribution patterns match real deployments.

major comments (2)

[Evaluation] Evaluation section (benchmarks of end-to-end performance): The central 3.8× speedup claim rests on the tested network conditions being representative of production cross-silo FL. The manuscript states that experiments use 'realistic network conditions' but provides no explicit validation, such as comparison to measured inter-silo RTT distributions, tail latencies, or bandwidth traces from actual deployments. This directly affects whether the reported gains generalize beyond the simulated environment.
[Design of gRPC+S3] § on gRPC+S3 hybrid design and point-to-point benchmarks: The hybrid backend is presented as overcoming limitations of pure gRPC for large models, yet the paper does not quantify the additional overhead (e.g., S3 upload/download latency or metadata costs) introduced by the object-store path versus a pure point-to-point approach. Without these measurements, it is unclear whether the net speedup holds under varying model-partitioning strategies or more frequent small-gradient exchanges typical in some FL workloads.

minor comments (2)

[Abstract] Abstract and evaluation: The abstract mentions 'a broad range of model sizes' and 'realistic network conditions' but does not list the exact parameter counts, model architectures, or the precise latency/bandwidth values used in the simulations.
[Evaluation] The manuscript should include error bars, number of runs, and statistical methods for the reported speedups to allow readers to assess variability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and have revised the manuscript to provide additional validation and measurements.

read point-by-point responses

Referee: [Evaluation] Evaluation section (benchmarks of end-to-end performance): The central 3.8× speedup claim rests on the tested network conditions being representative of production cross-silo FL. The manuscript states that experiments use 'realistic network conditions' but provides no explicit validation, such as comparison to measured inter-silo RTT distributions, tail latencies, or bandwidth traces from actual deployments. This directly affects whether the reported gains generalize beyond the simulated environment.

Authors: We agree that explicit validation would strengthen the generalizability of the results. In the revised manuscript we have added a new paragraph in the Evaluation section that directly compares our chosen RTT and bandwidth values to published inter-region measurements from major cloud providers (AWS, GCP) that are representative of cross-silo deployments. We also include a sensitivity analysis showing how the reported speedups vary across a range of realistic latency/bandwidth combinations drawn from the literature. While we do not have access to proprietary production traces, the added material makes the basis for our “realistic” label transparent. revision: yes
Referee: [Design of gRPC+S3] § on gRPC+S3 hybrid design and point-to-point benchmarks: The hybrid backend is presented as overcoming limitations of pure gRPC for large models, yet the paper does not quantify the additional overhead (e.g., S3 upload/download latency or metadata costs) introduced by the object-store path versus a pure point-to-point approach. Without these measurements, it is unclear whether the net speedup holds under varying model-partitioning strategies or more frequent small-gradient exchanges typical in some FL workloads.

Authors: We thank the referee for this observation. The revised version now contains an explicit latency breakdown for the gRPC+S3 hybrid in the point-to-point benchmark subsection, reporting S3 upload/download times and metadata overhead separately for each model size. These data show that the hybrid path yields a net gain once model size exceeds approximately 500 MB; for smaller models or high-frequency small-gradient exchanges we explicitly recommend the pure gRPC backend. We have also added results for partitioned-model transmission to demonstrate that the net benefit persists under different partitioning strategies. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical benchmarks with no derivations or self-referential reductions

full rationale

The paper presents in-depth benchmarks of communication backends (MPI, gRPC, PyTorch RPC) and introduces gRPC+S3 as a hybrid for large models in geo-distributed settings, reporting measured end-to-end speedups up to 3.8×. No equations, derivations, fitted parameters, predictions, or self-citations appear in the abstract or described content. All claims rest on direct experimental measurements under stated network conditions and model sizes, with no load-bearing step that reduces by construction to prior inputs or self-referential definitions. The analysis is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The paper relies on standard domain assumptions about network behavior and introduces one new design artifact; no free parameters are fitted to data.

axioms (1)

domain assumption Network latency, bandwidth, and geo-distribution patterns in the testbed match those encountered in real cross-silo deployments
Invoked to justify that benchmark conditions are realistic.

invented entities (1)

gRPC+S3 hybrid backend no independent evidence
purpose: Combine gRPC control plane with S3 object storage to handle large model transfers more efficiently than pure RPC or MPI in geo-distributed FL
New design proposed to overcome stated limitations of existing backends for large models.

pith-pipeline@v0.9.0 · 5509 in / 1340 out tokens · 61090 ms · 2026-05-10T14:57:22.461596+00:00 · methodology

discussion (0)

Reference graph

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