Presear builds distributed and federated AI systems — enabling collaborative model training across multiple sites, organisations, or devices while preserving data privacy and sovereignty.
Technical Depth
From FedAvg to Byzantine-robust training — we build distributed AI systems where collaboration never requires compromising data sovereignty.
Implement the canonical federated learning algorithm — each participant trains locally on private data, shares only model weight updates (gradients), and a central aggregator computes the weighted average to produce a global model. We tune FedAvg for heterogeneous data distributions (non-IID), partial client participation, and communication-efficient gradient compression to make the protocol robust in real enterprise deployments.
Provide mathematically provable privacy guarantees by injecting calibrated noise into gradient updates — ensuring that no individual record in any participant's dataset can be inferred from the shared model parameters. We tune privacy budgets (epsilon, delta) to achieve the required privacy level while minimising the accuracy impact, and provide formal privacy accounting using the moments accountant and RDP mechanisms.
Ensure that even the aggregation server cannot observe individual clients' gradient updates by implementing cryptographic secure aggregation — using secret sharing, homomorphic encryption, or trusted execution environments (TEEs). Secure aggregation is critical in adversarial settings where the aggregation server itself cannot be fully trusted, providing stronger privacy guarantees than differential privacy alone.
Handle both federation dimensions: horizontal FL (same features, different data samples across organisations — e.g., multiple hospitals with the same EHR schema) and vertical FL (different features, overlapping samples — e.g., a bank and retailer sharing the same customers). Each requires distinct architecture and privacy protocols; we design the appropriate system based on your data topology.
Deploy split learning architectures where the neural network is divided between client and server — the client processes raw data through early layers and sends intermediate activations (smashed data) rather than gradients or raw inputs. This dramatically reduces client-side compute requirements, making it feasible for resource-constrained participants while providing a different privacy-utility tradeoff compared to FedAvg-style approaches.
Defend against malicious or faulty participants that submit poisoned gradient updates designed to corrupt the global model. We implement Byzantine-robust aggregation algorithms — Krum, Trimmed Mean, FLTrust — that identify and down-weight anomalous updates before aggregation, ensuring that a minority of compromised clients cannot sabotage the shared model's performance or inject backdoors into the trained weights.
Our Process
A rigorous five-stage process. Click any step to explore what happens — and why it matters.
Every federated AI project begins with a thorough audit of participating data silos — understanding schema compatibility, data volume and quality at each site, regulatory classification of each dataset, and connectivity constraints between participants. This mapping determines whether horizontal or vertical federation is required and identifies data heterogeneity challenges that will affect model convergence.
Privacy requirements in federated systems are multi-dimensional: regulatory compliance (GDPR, HIPAA, DPDP), contractual obligations between participants, and technical threat models (honest-but-curious aggregators, external adversaries). We formalise the threat model and determine the appropriate privacy mechanism — differential privacy, secure aggregation, or TEE-based execution — along with the privacy budget each participant can accept.
We design the complete federated system architecture — selecting the FL framework (PySyft, TFF, Flower, FATE), designing the aggregation topology (star, hierarchical, peer-to-peer), specifying communication rounds and local epoch counts, and defining the model architecture's compatibility with federated constraints. The design includes failure handling for client dropouts and Byzantine participant detection.
We deploy and orchestrate the federated training process — provisioning secure communication channels between participants, managing participant authentication and authorisation, monitoring training progress across all clients, and handling the practical challenges of real-world federated deployments: intermittent connectivity, mismatched software versions, and heterogeneous compute capacities across participating nodes.
After convergence, the globally aggregated model undergoes rigorous evaluation — tested against held-out data at each participant site to verify that federated training achieved comparable accuracy to centralised training. The final model is containerised, versioned in a shared registry with cryptographic provenance attestation, and deployed to each participant site (or a shared inference endpoint) with monitoring for post-deployment performance.
Real-World Impact
Production federated AI deployments across regulated industries — where data collaboration was previously impossible.
Core Challenge
Training AI diagnostic models that generalise across patient populations requires data from multiple hospitals — but patient health records are legally protected and institutionally siloed. No hospital can share identifiable patient data with others, making centralised training legally and ethically impossible without a privacy-preserving alternative.
Who Benefits
Hospital networks, multi-site clinical research consortia, and health-tech companies developing diagnostic AI that requires population diversity beyond what any single institution's dataset can provide — without violating HIPAA, GDPR, or national health data regulations.
Request Case StudyCore Challenge
Fraudsters operate across multiple financial institutions simultaneously — but anti-fraud models trained on a single bank's transaction data miss cross-institution fraud patterns. Competing banks cannot share customer transaction data with each other due to regulatory and competitive constraints, leaving each institution with an incomplete picture of fraud networks.
Who Benefits
Commercial banks, payment networks, and fintech consortia that want to collaboratively detect cross-institution fraud rings and money laundering patterns without sharing individual customer transaction records — improving detection rates while maintaining full competitive and regulatory data confidentiality.
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Autonomous vehicle perception models need training data covering rare edge cases — unusual road conditions, atypical pedestrian behaviour, unusual weather — that no single OEM's fleet encounters frequently enough in isolation. Yet vehicle manufacturers cannot share raw sensor data (LiDAR, camera) with competitors due to IP and competitive concerns.
Who Benefits
Automotive OEMs, tier-1 suppliers, and mobility consortia building ADAS and autonomous driving AI systems that need cross-fleet training data diversity to improve rare-event handling — without ceding proprietary sensor data or training datasets to competitors or cloud providers.
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Government departments and agencies hold complementary datasets — tax records, health data, education data, social benefits — that collectively could power powerful welfare and fraud detection AI. But cross-ministry data sharing is restricted by legislation, creating siloed AI that lacks the cross-domain context needed to address multi-dimensional policy challenges.
Who Benefits
National and state government agencies, regulatory bodies, and public sector data trusts that need to build policy AI from multi-ministry datasets while complying with data protection legislation — enabling evidence-based governance without creating centralised citizen data warehouses.
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Frequently Asked
Answers to the questions data privacy officers, CTOs, and ML engineers ask before starting a federated AI engagement with Presear Softwares.
Ask Our Federated AI TeamPartner with Presear Softwares to build federated AI systems that unlock cross-organisational intelligence while keeping your data exactly where it belongs.