FedMM: Federated Collaborative Signal Quantization for Multi-Market CTR Prediction

• URL: http://arxiv.org/abs/2605.11433v1
• Date: Tue, 12 May 2026 02:32:43 GMT
• Title: FedMM: Federated Collaborative Signal Quantization for Multi-Market CTR Prediction
• Abstract summary: Online platforms such as Amazon and Netflix serve users across multiple countries and regions, underscoring the importance of multi-market recommendation (MMR)<n>Most MMR methods adopt a pre-training and fine-tuning paradigm, in which a unified model is first trained on centralized, global data.<n>We propose a federated collaborative signal quantization (FedMM) method for multi-market click-through rate (CTR) prediction.
• Score: 17.392497883316818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online platforms such as Amazon and Netflix serve users across multiple countries and regions, underscoring the importance of multi-market recommendation (MMR). Most MMR methods adopt a pre-training and fine-tuning paradigm, in which a unified model is first trained on centralized, global data and subsequently adapted to specific markets. However, this approach ignores the privacy of market data. While traditional federated learning preserves privacy, it typically aims to obtain a global model by aggregating model parameters and does not account for significant market heterogeneity. Additionally, because ID spaces are disjoint across markets, embedding-based aggregation strategies become ineffective. To overcome these challenges, we propose a federated collaborative signal quantization (FedMM) method for multi-market click-through rate (CTR) prediction. Our core idea leverages a discrete codebook mechanism to achieve privacy-preserving transmission and align disjoint ID spaces. We further employ a hierarchical codebook structure to capture cross-market shared patterns and market-specific characteristics. Specifically, we deploy a residual quantized variational autoencoder (RQ-VAE) with a dual-layer codebook mechanism for each market to quantize collaborative embeddings. The first layer utilizes a global federated codebook, updated via aggregation to capture universally shared collaborative patterns, while the second layer maintains a local codebook to learn market-specific semantics. Finally, the learned discrete codes, which integrate both general and specific collaborative signals, are incorporated into downstream CTR models to enhance prediction accuracy across all markets. Extensive experiments on benchmark datasets demonstrate that FedMM significantly improves recommendation performance with privacy guarantees.

Related papers

• Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity [12.733972494875713]
  FedRoRA is a novel framework that enables fine-grained personalization within rank-heterogeneous federations.<n>It decouples adaptation into shared global directions and personalized rank-wise magnitudes governed by learnable diagonal scales.<n>Experiments on NLU and NLG benchmarks demonstrate that FedRoRA consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-09-01T03:09:20Z)
• FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning [76.62016339908384]
  We propose FedHarmony, a framework that harmonizes heterogeneous label correlations across clients.<n>During aggregation, FedHarmony evaluates each client by both data size and correlation quality, assigning weights accordingly.<n>Experiments on real-world federated multi-label datasets show that FedHarmony consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-04-30T15:42:17Z)
• From Transfer to Collaboration: A Federated Framework for Cross-Market Sequential Recommendation [31.418148911609094]
  Cross-market recommendation (CMR) aims to enhance recommendation performance across multiple markets.<n>Existing CMR approaches largely inherit cross-domain recommendation (CDR)<n>We propose FeCoSR, a novel federated collaboration framework for cross-market sequential recommendation.
  arXiv  Detail & Related papers  (2026-04-15T07:33:50Z)
• A Step Toward Federated Pretraining of Multimodal Large Language Models [87.64508862413565]
  Federated Learning offers a promising solution to unlock distributed resources.<n>Fed-MA is a lightweight pre-training paradigm that freezes the vision encoder and LLM while collaboratively training the cross-modal projector.<n>We propose Fed-CMP, a pioneering framework for federated MLLM pre-training.
  arXiv  Detail & Related papers  (2026-03-25T08:16:23Z)
• FedDis: A Causal Disentanglement Framework for Federated Traffic Prediction [14.618528630951579]
  We introduce FedDis, a novel framework for federated spatial-temporal prediction.<n>We show that FedDis consistently achieves state-of-the-art performance datasets, promising efficiency, and superior expandability.
  arXiv  Detail & Related papers  (2026-01-30T05:20:53Z)
• FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modeling [52.05684032172943]
  We propose FedBook, a unified graph foundation codebook that aggregates clients' local codebooks during server-side federated pre-training.<n>FedBook consistently outperforms 21 baselines, including isolated supervised learning, FL/FGL, federated adaptations of centralized GFMs, and FedGFM techniques.
  arXiv  Detail & Related papers  (2025-10-09T03:50:30Z)
• OmniFC: Rethinking Federated Clustering via Lossless and Secure Distance Reconstruction [10.326927761316986]
  Federated clustering aims to discover global cluster structures across decentralized clients without sharing raw data.<n>There are two critical challenges: (1) privacy leakage during collaboration, and (2) robustness degradation due to aggregation of proxy information.<n>We propose Omni Federated Clustering, a unified and model-agnostic framework.
  arXiv  Detail & Related papers  (2025-05-19T13:04:59Z)
• One-Shot Federated Unsupervised Domain Adaptation with Scaled Entropy Attention and Multi-Source Smoothed Pseudo Labeling [21.59850502993888]
  Federated Learning (FL) is a promising approach for privacy-preserving collaborative learning.<n>We propose a one-shot Federated Unsupervised Domain Adaptation (FUDA) method to address these limitations.<n>Specifically, we introduce Scaled Entropy Attention (SEA) for model aggregation and Multi-Source Pseudo Labeling (MSPL) for target domain adaptation.
  arXiv  Detail & Related papers  (2025-03-13T03:59:51Z)
• Overcoming label shift with target-aware federated learning [10.355835466049092]
  Federated learning enables multiple actors to collaboratively train models without sharing private data.<n>A common reason is label shift -- that the label distributions differ between clients and the target domain.<n>We propose FedPALS, a principled and practical model aggregation scheme that adapts to label shifts to improve performance in the target domain.
  arXiv  Detail & Related papers  (2024-11-06T09:52:45Z)
• Federated Generalized Category Discovery [68.35420359523329]
  Generalized category discovery (GCD) aims at grouping unlabeled samples from known and unknown classes. To meet the recent decentralization trend in the community, we introduce a practical yet challenging task, namely Federated GCD (Fed-GCD) The goal of Fed-GCD is to train a generic GCD model by client collaboration under the privacy-protected constraint.
  arXiv  Detail & Related papers  (2023-05-23T14:27:41Z)
• Semi-Supervised Domain Generalization with Stochastic StyleMatch [90.98288822165482]
  In real-world applications, we might have only a few labels available from each source domain due to high annotation cost. In this work, we investigate semi-supervised domain generalization, a more realistic and practical setting. Our proposed approach, StyleMatch, is inspired by FixMatch, a state-of-the-art semi-supervised learning method based on pseudo-labeling.
  arXiv  Detail & Related papers  (2021-06-01T16:00:08Z)
• Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation [78.28390172958643]
  We identify two key aspects that can help to alleviate multiple domain-shifts in the multi-target domain adaptation (MTDA) We propose Curriculum Graph Co-Teaching (CGCT) that uses a dual classifier head, with one of them being a graph convolutional network (GCN) which aggregates features from similar samples across the domains. When the domain labels are available, we propose Domain-aware Curriculum Learning (DCL), a sequential adaptation strategy that first adapts on the easier target domains, followed by the harder ones.
  arXiv  Detail & Related papers  (2021-04-01T23:41:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.