Cross-Device Compatibility: Challenges & Methods

• Cross-device compatibility is a property that ensures systems operate consistently across heterogeneous devices, addressing differences in hardware, software, and interfaces.
• It involves overcoming challenges in identity linkage, asynchronous communication, and secure aggregation to support unified user modeling and personalized interactions.
• Practical solutions include using siamese networks, federated learning protocols, and virtual-camera rectification to bolster system robustness in varying environments.

Cross-device compatibility is the property of a system, method, or model to function correctly, efficiently, and robustly across multiple hardware and software platforms—often with heterogeneous capabilities and interfaces. This property is essential in modern computational environments, where user journeys, distributed computation, sensing, authentication, and user interfaces span a diversity of devices (smartphones, wearables, desktops, IoT devices, AR/VR headsets, specialized sensors). Achieving cross-device compatibility poses significant algorithmic, systems, and evaluation challenges owing to heterogeneity in form-factor, compute power, input/output modalities, operating system, and network characteristics. Research on arXiv has addressed cross-device compatibility at varying levels: user identification, federated learning, privacy and security aggregation, GUI testing, physiological authentication, sensor fusion, omnidirectional vision, side-channel forensics, and eye-tracking.

1. Problem Definitions and Fundamental Challenges

Cross-device compatibility encompasses two primary aspects: functional equivalence—the system produces correct and comparable results across devices—and operational robustness—the system remains efficient, secure, and scalable despite device heterogeneity and diverse runtime constraints. In user-centric applications such as personalization and advertising, compatibility requires linking identifiers or activities from multiple devices to a single real-world user, overcoming fragmentation and enabling comprehensive modeling (Tanielian et al., 2018). In federated learning, compatibility extends to the algorithm’s ability to handle non-IID data, variable hardware capabilities, intermittent connectivity, and straggler effects across clients ranging from resource-rich desktops to constrained embedded sensors (Chen et al., 2023, Wang et al., 2024, Guo et al., 2024).

Key technical challenges include:

• Data fragmentation and identity linkage: Activity tokens or event logs are siloed under device-specific IDs (e.g., browser cookies, device tokens).
• Hardware and software heterogeneity: Differences in CPU/GPU architectures, memory, OS APIs, input/output capabilities, sensors, and display.
• Communication and synchronization diversity: Variable bandwidth, transient connections, different protocols, and latency profiles.
• Security and privacy requirements: Ensuring robust aggregation or model sharing without leaking sensitive data across device boundaries (Wang et al., 2024, Guo et al., 2024).
• Domain gap in signals and measurements: Sensor modalities, sampling rates, calibration, noise, and environmental effects may differ and affect downstream models (Huang et al., 2023, Wu et al., 8 Feb 2025).

2. Architectures and Methods for Cross-Device User Modeling

One canonical cross-device compatibility task is matching device-specific observations to reconstruct unified user journeys. The Siamese Cookie Embedding Network (SCEmNet) formulates cross-device user matching as a supervised, siamese-style learning problem, embedding multi-modal event histories (e.g., URL sequences at multiple granularities) for each cookie/device and learning a similarity score that identifies shared real-world users (Tanielian et al., 2018). SCEmNet’s convolutional architecture handles arbitrarily many sequence modalities, allowing event-level information from mobile, desktop, and tablet to be fused. The key technical principle is multi-modal sequence embedding (SeqCNN), followed by pairwise fusion using element-wise multiplication (Hadamard product) and concatenation across modalities, enabling robust matching despite device and domain-specific variations. Joint training with classical features further boosts F1 by ∼4 points over strong baselines in large-scale experiments (Table in (Tanielian et al., 2018)), demonstrating that supervised representation learning directly strengthens cross-device compatibility in personalization and attribution.

3. Federated and Distributed Learning Across Heterogeneous Devices

Federated learning poses stringent requirements for cross-device compatibility. “FS-Real” explicitly models device runtime heterogeneity across CPU/GPU types, memory, bandwidth, and availability, and introduces system-level features including parallel and robust servers, asynchronous aggregation, and utility-driven adaptation (Chen et al., 2023). Asynchronous federated learning (AFL) addresses straggler bottlenecks by decoupling progress from the slowest device; however, secure aggregation—crucial for privacy—has historically been synchronous, blocking the scalable deployment of AFL in cross-device settings.

Buffer Asynchronous Secure Aggregation (BASA) provides the first provably secure aggregation protocol fully compatible with AFL, requiring only single-round masked uploads and no peer-to-peer channels. BASA attains near-linear scalability (per-user computation scaling only in buffer size, independent of global population) and strong privacy guarantees even under collusions, outperforming synchronous secure aggregation by up to 4.7× under high straggler rates (Wang et al., 2024).

Hierarchical federated graph learning (HiFGL) extends compatibility to settings with simultaneously cross-silo and cross-device graphs. HiFGL uses a unified three-tier architecture (server, silos, and devices), secret message passing based on Lagrange-coded secret sharing, and neighbor-agnostic aggregation to ensure subgraph- and node-level privacy while keeping per-device computation and communication tractable. This scheme allows devices of very low computational capability to securely participate in federated GNN training with minimal per-edge overhead (Guo et al., 2024).

4. Evaluation, Benchmarking, and Adaptation in Real-world Heterogeneity

Empirical research emphasizes the necessity of realistic benchmarking under diverse device profiles and scales. FS-Real demonstrates that naive simulation on homogeneous infrastructure underestimates accuracy gaps, convergence delays, and network costs present in large-scale, real-world federated systems—gaps in accuracy (up to 2.7%), fairness (7%), utilization (10–20%), and time-to-convergence (variance up to 1.34 h) manifest as the number of heterogeneous clients scales into the thousands (Chen et al., 2023). Asynchronous aggregation and adaptive compression (INT8, FP16) partially mitigate these inefficiencies, and system-level innovations (timeout, over-selection, robust server concurrency) prove essential for high utilization and robust convergence in the presence of stragglers and variable network conditions.

5. Cross-Device Compatibility in Sensing, Authentication, and Human-Computer Interaction

Biometric Authentication and Sensor Fusion

Transfer of authentication status across heterogeneous devices is a major compatibility use case. PPGTransID leverages the real-time physiological consistency of photoplethysmography (PPG) signals across the human body to realize cross-device authentication for smart wearables (Liu et al., 9 Feb 2026). It bridges trusted smartphone platforms (with robust on-device authentication) and resource-constrained wearables using synchronous comparison of rPPG (remote, camera-based) and local PPG signals. Its pre-processing (bandpass, z-normalization, channel selection, MA filtering), statistical feature extraction (21 time- and frequency-domain features), and machine-learning-based classification (XGBoost, BAC of 95.5%) ensure form-factor-agnostic authentication across rings, bands, earphones, and glasses, generalizing to unseen devices with ≤0.6% drop in BAC and minimal need for enrollment.

Electromagnetic Forensics and Robust Model Portability

Cross-device model transfer is also critical in digital forensics. EM side-channel analysis (EM-SCA) reveals that ML models trained on traces from one device instance yield poor accuracy (<20%) on other nominally identical devices. Domain-adaptive transfer learning—freezing all but the output layer and fine-tuning on a small calibration set—recovers accuracy to >98% for iPhone 13 and 96% for nRF52-DK, solving the cross-device portability problem with minimal overhead (Navanesana et al., 2023).

6. Cross-Device Generalization in Perceptual, Localization, and UI Testing Tasks

Visual Perception and Attention

Wearable and high-end eye-trackers differ in sampling rate, calibration, and noise. Studies show that average fixation predictions transfer robustly across devices for simple stimuli (AUC-Judd 0.837; CC 0.496; NSS 1.666; SIM 0.436) (Wu et al., 8 Feb 2025), supporting the use of portable hardware in group-level saliency modeling. Individual-level prediction consistency, however, remains limited by device noise and intersubject variability, leading to weaker clinical or personalized applications.

Visual Localization with Heterogeneous Imaging

360Loc presents the first omnidirectional localization benchmark with heterogeneous query and reference devices (pinhole, fisheye, and 360°). Virtual camera synthesis aligns the domain between 360°-reference and arbitrary query camera types, mitigating the cross-device gap in feature matching and pose regression. VC2 rectification doubles R@1 for pinhole queries (from 0.23 to 0.50), and training on VC2 crops reduces APR error by up to 80%, recommending virtual-camera augmentation as an essential cross-device compatibility strategy (Huang et al., 2023).

GUI Testing, Holographic Interaction, and Dynamic Interfaces

Robust GUI testing across devices and platforms is enabled by multi-modal widget representations (spatial, visual, semantic, contextual, neighborhood) and non-intrusive, vision-based techniques. NiCro achieves F1 ≈ 0.91 (widget matching) and an end-to-end 0-correction replay rate of 63% on eight different devices (Android/iOS, phone/tablet/emulator) by combining advanced OCR, visual embeddings, container detection, and adaptive matching, tolerating layout and platform divergence (Xie et al., 2023). For interactive and immersive settings, HoloDevice supports real-time remote cross-device manipulation via unified coordinate systems, event synchronization, and rich visual affordances, guaranteeing compatibility and low latency (motion update rate 60 Hz, latency ~13–25 ms) (Chulpongsatorn et al., 2024).

7. Summary Table: Key Domains and Cross-Device Solutions

Domain	Key Challenge	Cross-Device Solution	Source
User modeling	Identity fragmentation	Siamese embedding, multi-modal CNN, joint feature fusion	(Tanielian et al., 2018)
Distributed learning	Heterogeneity, privacy	Asynchronous FL, secure aggregation (BASA), hierarchical GNN	(Chen et al., 2023Wang et al., 2024Guo et al., 2024)
Authentication	Sensor and form factor	Synchronous multimodal PPG, ML matching, robust to device variance	(Liu et al., 9 Feb 2026)
Forensic analysis	Device-level domain gap	Transfer learning, output-layer fine-tuning on small sample	(Navanesana et al., 2023)
Visual localization	Camera type domain gap	Virtual-camera rectification, pose/feature alignment	(Huang et al., 2023)
GUI/Interaction testing	Layout/OS divergence	Multi-modal image-based widget matching, non-intrusive robot farm	(Xie et al., 2023)
Perceptual modeling	Tracker/sensor mismatch	Population-average fixation transfer, calibration-aware evaluation	(Wu et al., 8 Feb 2025)

• (Tanielian et al., 2018) Siamese Cookie Embedding Networks for Cross-Device User Matching
• (Chen et al., 2023) FS-Real: Towards Real-World Cross-Device Federated Learning
• (Wang et al., 2024) Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning
• (Guo et al., 2024) HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning
• (Liu et al., 9 Feb 2026) PPG as a Bridge: Cross-Device Authentication for Smart Wearables with Photoplethysmography
• (Navanesana et al., 2023) Ensuring Cross-Device Portability of Electromagnetic Side-Channel Analysis
• (Huang et al., 2023) 360Loc: A Dataset and Benchmark for Omnidirectional Visual Localization with Cross-device Queries
• (Xie et al., 2023) NiCro: Purely Vision-based, Non-intrusive Cross-Device and Cross-Platform GUI Testing
• (Wu et al., 8 Feb 2025) Evaluating Cross-Subject and Cross-Device Consistency in Visual Fixation Prediction