mlsanityData to 14 Oct 2026

Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Need

arXiv 2410.17648 ↗First posted 23 Oct 2024cs.LGcited by 2

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Abstract (arXiv)

Existing Vertical FL (VFL) methods often struggle with realistic and unaligned data partitions, and incur into high communication costs and significant operational complexity. This work introduces a novel approach to VFL, Active Participant Centric VFL (APC-VFL), that excels in scenarios when data samples among participants are partially aligned at training. Among its strengths, APC-VFL only requires a single communication step with the active participant. This is made possible through a local and unsupervised representation learning stage at each participant followed by a knowledge distillation step in the active participant. Compared to other VFL methods such as SplitNN or VFedTrans, APC-VFL consistently outperforms them across three popular VFL datasets in terms of F1, accuracy and communication costs as the ratio of aligned data is reduced.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 32 of its 47 references that are in the map. First posted on arXiv 23 Oct 2024.

Builds on

32 of 47 references.

References · 32

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20 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Vertical Federated Learning in Practice: The Good, the Bad, and the Uglycited by 4
  2. 2026Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learningcited by 0

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Close in wording, no citation either way

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