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Cross-Silo Federated Learning: Challenges and Opportunities

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arxiv 2206.12949 v1 pith:XUUPWYSZ submitted 2022-06-26 cs.LG cs.AIcs.GT

Chao Huang , Jianwei Huang , Xin Liu This is my paper · ORCID
classification cs.LGcs.AIcs.GT
keywords cross-siloclientslearningchallengescross-devicefederatedclientdiscuss
verification ladder T0 review T1 audit T2 compute T3 formal
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Federated learning (FL) is an emerging technology that enables the training of machine learning models from multiple clients while keeping the data distributed and private. Based on the participating clients and the model training scale, federated learning can be classified into two types: cross-device FL where clients are typically mobile devices and the client number can reach up to a scale of millions; cross-silo FL where clients are organizations or companies and the client number is usually small (e.g., within a hundred). While existing studies mainly focus on cross-device FL, this paper aims to provide an overview of the cross-silo FL. More specifically, we first discuss applications of cross-silo FL and outline its major challenges. We then provide a systematic overview of the existing approaches to the challenges in cross-silo FL by focusing on their connections and differences to cross-device FL. Finally, we discuss future directions and open issues that merit research efforts from the community.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning

    cs.CR 2025-08 conditional novelty 6.0 of 10

    A malicious federated-learning client can hide a backdoor by adding trigger-labeled samples plus camouflage samples, then activate it by requesting unlearning of the camouflage samples.

  2. FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projection

    cs.LG 2025-09 reject novelty 5.0 of 10

    FedRP claims to preserve FedAvg-level accuracy while sending only a few numbers per client per round and providing an (epsilon, delta)-DP guarantee.

  3. PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning

    cs.LG 2025-02 conditional novelty 5.0 of 10

    PLayer-FL picks the layer split in partial federated learning from a low-cost sensitivity metric computed at epoch 1, and reports competitive F1, fairness, and participation incentives across seven non-IID datasets.

  4. FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning

    cs.CR 2025-07 reject novelty 4.0 of 10

    FedP3E shares noisy class prototypes across federated clients plus SMOTE augmentation, reporting 95.1 to 99.6% accuracy on N-BaIoT under non-IID splits, beating FedAvg and FedProx.

  5. Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption

    cs.CR 2025-05 conditional novelty 4.0 of 10

    MASER combines majority-vote weight pruning with multi-key homomorphic encryption to reduce privacy-preserving federated learning overhead by 3 to 8 times while keeping accuracy within about 1 percent of vanilla FL.

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