# Screen Before You Fetch: Compressed Byzantine Screening for Decentralized Learning on the Edge-Cloud Continuum

arXiv: https://arxiv.org/abs/2510.07922 · HTML: https://mlsanity.com/paper/2510.07922/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 22 of 22)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [signSGD: Compressed Optimisation for Non-Convex Problems](https://mlsanity.com/paper/1802.04434/) (2018)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication](https://mlsanity.com/paper/1902.00340/) (2019)
- [FetchSGD: Communication-Efficient Federated Learning with Sketching](https://mlsanity.com/paper/2007.07682/) (2020)
- [RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets](https://mlsanity.com/paper/1811.03761/) (2018)
- [Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation](https://mlsanity.com/paper/1903.03936/) (2019)
- [Byzantine-Robust Decentralized Federated Learning](https://mlsanity.com/paper/2406.10416/) (2024)
- [Byzantine-Robust Decentralized Learning via ClippedGossip](https://mlsanity.com/paper/2202.01545/) (2022)
- [Byzantine-resilient Decentralized Stochastic Gradient Descent](https://mlsanity.com/paper/2002.08569/) (2020)
- [Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach](https://mlsanity.com/paper/2209.00778/) (2022)
- [A Framework for Adversarially Robust Streaming Algorithms](https://mlsanity.com/paper/2003.14265/) (2020)
- [Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates](https://mlsanity.com/paper/2310.09804/) (2023)
- [Byzantine-Robust Aggregation for Securing Decentralized Federated Learning](https://mlsanity.com/paper/2409.17754/) (2024)
- [Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review](https://mlsanity.com/paper/2205.01081/) (2022)
- [BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model](https://mlsanity.com/paper/2310.13403/) (2023)

## Built on by (most cited first, 1 of 1)

- [Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT](https://mlsanity.com/paper/2512.19131/) (2025)

## Abstract (arXiv)

In decentralized federated learning, Byzantine-robust defenses filter neighbors by model similarity, but only after receiving each neighbor's full model. On constrained edge uplinks, that transfer dominates the round, even for discarded models. We propose SketchGuard, which screens neighbors on compact sketches and fetches full models only from those that pass, so a rejected neighbor costs O(k) rather than O(d) where kll d. Sketches, however, hide information an adversary can exploit, either by building a model indistinguishable from a benign one under a public compressor or by forging a sketch once the seed is known. Three rules close these gaps, namely exact verification of every fetched model, commitment before the sketch seed is drawn, and a ban on any sender whose model mismatches its sketch. We prove convergence in the strongly convex and non-convex settings with explicit topology and heterogeneity terms. Across three datasets and two wrapped filters, SketchGuard reproduces the uncompressed filter's test error to within 0.003, withstands a band-targeted attack that defeats sketch-only screening, and moves 0.7× the bytes of full exchange under attack for under 1.7% of a round in compute. On emulated edge and cloud links it cuts the round to 0.61× that of the state-of-the-art.