VBAF

5.0.0

VBAF.Enterprise.FederatedLearning.ps1

#Requires -Version 5.1
<#
.SYNOPSIS
    Phase 16 - Federated Learning Intelligence
.DESCRIPTION
    Trains a DQN agent to coordinate distributed model updates across
    multiple VBAF nodes without sharing raw data. The agent observes
    federation health signals and learns when to:
      - Collect : gather local model updates from nodes (action 0)
      - Aggregate: merge updates into global model (action 1)
      - Validate : verify model quality before deployment (action 2)
      - Rollback : reject bad updates, restore last good (action 3)
.NOTES
    Part of VBAF - Phase 16 Enterprise Automation Engine
    Phase 16: Federated Learning Intelligence
    PS 5.1 compatible
    Real data: Get-Job, network latency probes, WMI node health
#>

# ============================================================
# PHASE 16 - FEDERATED LEARNING
# ============================================================

class FederatedLearningEnvironment {

    # State: 4 genuinely observable federation health signals (0.0 - 1.0)
    # NO SeverityNorm — agent must learn the mapping from real signals
    [double] $ModelDivergence  # 0=nodes agree 1=models wildly different
    [double] $NodeAvailability # 1=all nodes down 0=all nodes healthy
    [double] $UpdateQuality    # 0=poor updates 1=high quality gradients (inverted — breaks monotonic collapse)
    [double] $FederationLoad   # 0=idle 1=aggregation saturated

    [int]    $CorrectActions
    [int]    $MissedAggregations
    [int]    $Steps
    [double] $TotalReward
    [int]    $EpisodeCount

    # Confusion matrix
    [int]    $TruePositives
    [int]    $FalsePositives
    [int]    $TrueNegatives
    [int]    $FalseNegatives

    [int]    $CurrentSeverity  # raw 0-3 (maps directly to optimal action)

    # Required by VBAF framework
    [int]    $StateSize  = 4
    [int]    $ActionSize = 4

    # Step() stores result here — avoids PSCustomObject type corruption (PS 5.1)
    [double] $LastReward = 0.0
    [bool]   $LastDone   = $false

    FederatedLearningEnvironment() {
        $this.Reset() | Out-Null
    }

    # CRITICAL PS 5.1: build strictly typed [double[]] element by element
    # NO SeverityNorm — real observable signals only
    [double[]] GetState() {
        [double[]] $s = @(0.0, 0.0, 0.0, 0.0)
        $s[0] = $this.ModelDivergence
        $s[1] = $this.NodeAvailability
        $s[2] = $this.UpdateQuality
        $s[3] = $this.FederationLoad
        return $s
    }

    [double[]] Reset() {
        $this.Steps               = 0
        $this.TotalReward         = 0.0
        $this.CorrectActions      = 0
        $this.MissedAggregations  = 0
        $this.TruePositives       = 0
        $this.FalsePositives      = 0
        $this.TrueNegatives       = 0
        $this.FalseNegatives      = 0
        $this.LastDone            = $false   # CRITICAL: must reset here
        $this.EpisodeCount++
        $this._SampleCondition()
        [double[]] $initState = $this.GetState()
        return $initState
    }

    [void] _SampleCondition() {
        # Balanced training distribution
        # 25% collect (0), 30% aggregate (1), 25% validate (2), 20% rollback (3)
        $roll = Get-Random -Minimum 1 -Maximum 100
        if      ($roll -le 25) { $this.CurrentSeverity = 0 }
        elseif  ($roll -le 55) { $this.CurrentSeverity = 1 }
        elseif  ($roll -le 80) { $this.CurrentSeverity = 2 }
        else                   { $this.CurrentSeverity = 3 }

        # Generate federation metrics consistent with severity
        # Well-separated ranges — no overlap that causes collapse
        switch ($this.CurrentSeverity) {
            0 {
                # Collect: low divergence, nodes healthy, HIGH quality, low load
                $this.ModelDivergence  = [double](Get-Random -Minimum 0  -Maximum 20) / 100.0
                $this.NodeAvailability = [double](Get-Random -Minimum 0  -Maximum 15) / 100.0
                $this.UpdateQuality    = [double](Get-Random -Minimum 80 -Maximum 100) / 100.0
                $this.FederationLoad   = [double](Get-Random -Minimum 0  -Maximum 25) / 100.0
            }
            1 {
                # Aggregate: moderate divergence, mostly available, decent quality
                $this.ModelDivergence  = [double](Get-Random -Minimum 25 -Maximum 50) / 100.0
                $this.NodeAvailability = [double](Get-Random -Minimum 15 -Maximum 40) / 100.0
                $this.UpdateQuality    = [double](Get-Random -Minimum 50 -Maximum 80) / 100.0
                $this.FederationLoad   = [double](Get-Random -Minimum 25 -Maximum 55) / 100.0
            }
            2 {
                # Validate: high divergence, some nodes down, quality uncertain
                $this.ModelDivergence  = [double](Get-Random -Minimum 50 -Maximum 75) / 100.0
                $this.NodeAvailability = [double](Get-Random -Minimum 40 -Maximum 65) / 100.0
                $this.UpdateQuality    = [double](Get-Random -Minimum 20 -Maximum 50) / 100.0
                $this.FederationLoad   = [double](Get-Random -Minimum 55 -Maximum 80) / 100.0
            }
            3 {
                # Rollback: very high divergence, nodes failing, LOW quality
                $this.ModelDivergence  = [double](Get-Random -Minimum 75  -Maximum 100) / 100.0
                $this.NodeAvailability = [double](Get-Random -Minimum 65  -Maximum 100) / 100.0
                $this.UpdateQuality    = [double](Get-Random -Minimum 0   -Maximum 20)  / 100.0
                $this.FederationLoad   = [double](Get-Random -Minimum 80  -Maximum 100) / 100.0
            }
        }
    }

    [int] _OptimalAction() {
        # 0=Collect 1=Aggregate 2=Validate 3=Rollback
        return $this.CurrentSeverity
    }

    [void] Step([int]$action) {
        $this.Steps++
        $optimal = $this._OptimalAction()

        # Symmetric distance-based reward (proven across Phases 10-15)
        [int] $dist = $action - $optimal
        if ($dist -lt 0) { $dist = -$dist }

        if    ($dist -eq 0) { $this.LastReward =  2.0; $this.CorrectActions++ }
        elseif($dist -eq 1) { $this.LastReward = -1.0 }
        elseif($dist -eq 2) { $this.LastReward = -2.0 }
        else                { $this.LastReward = -3.0 }

        if ($this.CurrentSeverity -ge 2 -and $action -lt 2) { $this.MissedAggregations++ }

        $isCritical  = ($this.CurrentSeverity -ge 2)
        $agentActs   = ($action -ge 2)
        if ($isCritical  -and $agentActs)  { $this.TruePositives++  }
        if (!$isCritical -and $agentActs)  { $this.FalsePositives++ }
        if (!$isCritical -and !$agentActs) { $this.TrueNegatives++  }
        if ($isCritical  -and !$agentActs) { $this.FalseNegatives++ }

        $this.TotalReward += $this.LastReward
        $this._SampleCondition()
        $this.LastDone = ($this.Steps -ge 200)
    }
}

# ------------------------------------
# Real Windows federation probe
# ------------------------------------
function Get-VBAFFederationSnapshot {
    [CmdletBinding()]
    param()

    Write-Host ""
    Write-Host " Probing federated learning node health..." -ForegroundColor Gray

    try {
        # Background jobs as proxy for federation nodes
        $jobs = Get-Job -ErrorAction SilentlyContinue
        $running  = ($jobs | Where-Object { $_.State -eq "Running" }).Count
        $complete = ($jobs | Where-Object { $_.State -eq "Completed" }).Count
        $failed   = ($jobs | Where-Object { $_.State -eq "Failed" }).Count
        Write-Host (" Active jobs (nodes) : {0}" -f $running)  -ForegroundColor White
        Write-Host (" Completed jobs : {0}" -f $complete) -ForegroundColor Green
        Write-Host (" Failed jobs : {0}" -f $failed)   -ForegroundColor $(if ($failed -gt 0) { "Red" } else { "Green" })

        # Network latency as model sync proxy
        $tc = Test-NetConnection -ComputerName "8.8.8.8" -WarningAction SilentlyContinue -ErrorAction Stop
        $latency = if ($tc.PingSucceeded) { $tc.PingReplyDetails.RoundtripTime } else { 9999 }
        $latColour = if ($latency -lt 50) { "Green" } elseif ($latency -lt 200) { "Yellow" } else { "Red" }
        Write-Host (" Network latency : {0}ms" -f $latency) -ForegroundColor $latColour

        # CPU as aggregation load proxy
        $cpu = Get-WmiObject -Class Win32_Processor -ErrorAction Stop | Select-Object -First 1
        Write-Host (" CPU load (agg proxy) : {0}%" -f $cpu.LoadPercentage) -ForegroundColor White
        Write-Host " Federation probe : confirmed ✅" -ForegroundColor Green

    } catch {
        Write-Host " [WARNING] Federation probe incomplete: $($_.Exception.Message)" -ForegroundColor Yellow
        Write-Host " [INFO] Training will use simulated federation conditions."   -ForegroundColor Gray
    }
}

# ============================================================
# MAIN TRAINING FUNCTION
# ============================================================
function Invoke-VBAFFederatedLearningTraining {
    param(
        [int]    $Episodes    = 100,
        [int]    $PrintEvery  = 10,
        [switch] $FastMode,
        [switch] $SimMode,
        [switch] $SkipRealData
    )

    Write-Host ""
    Write-Host "🔗 VBAF Enterprise - Phase 16: Federated Learning"                  -ForegroundColor Cyan
    Write-Host " Training DQN agent on distributed model coordination..."          -ForegroundColor Cyan
    Write-Host " Actions: 0=Collect 1=Aggregate 2=Validate 3=Rollback"         -ForegroundColor Yellow
    Write-Host " State : ModelDivergence | NodeAvail | UpdateQuality | FedLoad"  -ForegroundColor Yellow
    Write-Host " Reward : +2 correct -1 dist=1 -2 dist=2 -3 dist=3"           -ForegroundColor Yellow
    Write-Host ""

    if (-not $SkipRealData) {
        Get-VBAFFederationSnapshot
    }

    $flEnv = [FederatedLearningEnvironment]::new()

    # Phase 1: Baseline — inline random loop
    Write-Host " Phase 1: Baseline (random agent - 10 episodes)..." -ForegroundColor Gray
    $baseRewards = @()
    for ($b = 1; $b -le 10; $b++) {
        $flEnv.Reset() | Out-Null
        $bReward = 0.0
        while (-not $flEnv.LastDone) {
            $rAction  = Get-Random -Minimum 0 -Maximum 4
            $flEnv.Step($rAction)
            $bReward += $flEnv.LastReward
        }
        $baseRewards += $bReward
    }
    [double[]] $bAvgArr = @(0.0)
    $bAvgArr[0] = ($baseRewards | Measure-Object -Average).Average
    Write-Host (" Baseline avg reward: {0:F2}" -f $bAvgArr[0]) -ForegroundColor Gray

    if ($FastMode) { $Episodes = [Math]::Min($Episodes, 30) }
    Write-Host ""
    Write-Host " Phase 2: Training DQN agent ($Episodes episodes)..." -ForegroundColor Gray

    $config              = [DQNConfig]::new()
    $config.StateSize    = 4
    $config.ActionSize   = 4
    $config.EpsilonDecay = 0.9995
    $config.EpsilonMin   = 0.05
    [int[]] $arch        = @(4, 24, 24, 4)
    $mainNetwork         = [NeuralNetwork]::new($arch, $config.LearningRate)
    $targetNetwork       = [NeuralNetwork]::new($arch, $config.LearningRate)
    $memory              = [ExperienceReplay]::new($config.MemorySize)
    $agent               = [DQNAgent]::new($config, $mainNetwork, $targetNetwork, $memory)

    $results = [System.Collections.Generic.List[object]]::new()

    for ($ep = 1; $ep -le $Episodes; $ep++) {

        [double[]] $state = @(0.0, 0.0, 0.0, 0.0)

        if ($SimMode) {
            $roll = Get-Random -Minimum 1 -Maximum 100
            if      ($roll -le 25) { $flEnv.CurrentSeverity = 0 }
            elseif  ($roll -le 55) { $flEnv.CurrentSeverity = 1 }
            elseif  ($roll -le 80) { $flEnv.CurrentSeverity = 2 }
            else                   { $flEnv.CurrentSeverity = 3 }

            switch ($flEnv.CurrentSeverity) {
                0 {
                    $flEnv.ModelDivergence  = [double](Get-Random -Minimum 0  -Maximum 20) / 100.0
                    $flEnv.NodeAvailability = [double](Get-Random -Minimum 0  -Maximum 15) / 100.0
                    $flEnv.UpdateQuality    = [double](Get-Random -Minimum 80 -Maximum 100) / 100.0
                    $flEnv.FederationLoad   = [double](Get-Random -Minimum 0  -Maximum 25) / 100.0
                }
                1 {
                    $flEnv.ModelDivergence  = [double](Get-Random -Minimum 25 -Maximum 50) / 100.0
                    $flEnv.NodeAvailability = [double](Get-Random -Minimum 15 -Maximum 40) / 100.0
                    $flEnv.UpdateQuality    = [double](Get-Random -Minimum 50 -Maximum 80) / 100.0
                    $flEnv.FederationLoad   = [double](Get-Random -Minimum 25 -Maximum 55) / 100.0
                }
                2 {
                    $flEnv.ModelDivergence  = [double](Get-Random -Minimum 50 -Maximum 75) / 100.0
                    $flEnv.NodeAvailability = [double](Get-Random -Minimum 40 -Maximum 65) / 100.0
                    $flEnv.UpdateQuality    = [double](Get-Random -Minimum 20 -Maximum 50) / 100.0
                    $flEnv.FederationLoad   = [double](Get-Random -Minimum 55 -Maximum 80) / 100.0
                }
                3 {
                    $flEnv.ModelDivergence  = [double](Get-Random -Minimum 75  -Maximum 100) / 100.0
                    $flEnv.NodeAvailability = [double](Get-Random -Minimum 65  -Maximum 100) / 100.0
                    $flEnv.UpdateQuality    = [double](Get-Random -Minimum 0   -Maximum 20)  / 100.0
                    $flEnv.FederationLoad   = [double](Get-Random -Minimum 80  -Maximum 100) / 100.0
                }
            }
            $flEnv.CorrectActions     = 0
            $flEnv.MissedAggregations = 0
            $flEnv.Steps              = 0
            $flEnv.TotalReward        = 0.0
            $flEnv.LastDone           = $false
            $flEnv.EpisodeCount++
            $state = $flEnv.GetState()
        } else {
            $state = $flEnv.Reset()
        }

        $done            = $false
        $epReward        = 0.0
        $collectCount    = 0
        $aggregateCount  = 0
        $validateCount   = 0
        $rollbackCount   = 0
        [int] $stepCount = 0

        while (-not $done) {
            $action = $agent.Act($state)
            $flEnv.Step($action)
            [double[]] $nextState = $flEnv.GetState()
            [double]   $reward    = $flEnv.LastReward
            [bool]     $isDone    = $flEnv.LastDone
            $agent.Remember($state, $action, $reward, $nextState, $isDone)
            $stepCount++
            if ($stepCount % 4 -eq 0) { $agent.Replay() }
            $state     = $nextState
            $done      = $isDone
            $epReward += $reward
            switch ($action) {
                0 { $collectCount++   }
                1 { $aggregateCount++ }
                2 { $validateCount++  }
                3 { $rollbackCount++  }
            }
        }

        $agent.EndEpisode($epReward)
        $results.Add(@{
            Episode   = $ep
            Reward    = $epReward
            Collect   = $collectCount
            Aggregate = $aggregateCount
            Validate  = $validateCount
            Rollback  = $rollbackCount
            Epsilon   = $agent.Epsilon
        })

        if ($ep % $PrintEvery -eq 0) {
            $lastN  = $results | Select-Object -Last $PrintEvery
            $avgSum = 0.0
            foreach ($r2 in $lastN) { $avgSum += $r2.Reward }
            [double[]] $avgArr = @(0.0)
            $avgArr[0]  = $avgSum
            $avgArr[0] /= $lastN.Count
            $avg = [Math]::Round($avgArr[0], 2)
            Write-Host (" Ep {0,4}/{1} AvgReward: {2,7} Eps: {3:F3} Col:{4} Agg:{5} Val:{6} Rol:{7}" -f `
                $ep, $Episodes, $avg, $agent.Epsilon, $collectCount, $aggregateCount, $validateCount, $rollbackCount) -ForegroundColor White
        }
    }

    # Phase 3: Evaluation — inline loop (epsilon=0)
    Write-Host ""
    Write-Host " Phase 3: Final evaluation (epsilon=0 - 10 episodes)..." -ForegroundColor Gray
    $agent.Epsilon = 0.0
    $trainedRewards = @()
    for ($t = 1; $t -le 10; $t++) {
        [double[]] $evalState = $flEnv.Reset()
        $tReward = 0.0
        while (-not $flEnv.LastDone) {
            $tAction = $agent.Act($evalState)
            $flEnv.Step($tAction)
            [double[]] $evalState = $flEnv.GetState()
            $tReward += $flEnv.LastReward
        }
        $trainedRewards += $tReward
    }
    [double[]] $tAvgArr = @(0.0)
    $tAvgArr[0] = ($trainedRewards | Measure-Object -Average).Average
    Write-Host (" Trained avg reward: {0:F2}" -f $tAvgArr[0]) -ForegroundColor Green

    [double[]] $impArr = @(0.0)
    if ($bAvgArr[0] -ne 0) {
        $impArr[0]  = $tAvgArr[0] - $bAvgArr[0]
        $impArr[0] /= [Math]::Abs($bAvgArr[0])
        $impArr[0] *= 100.0
    }
    $bAvg        = [Math]::Round($bAvgArr[0], 2)
    $tAvg        = [Math]::Round($tAvgArr[0], 2)
    $improvement = [Math]::Round($impArr[0], 1)

    [double[]] $precArr = @(0.0)
    [double[]] $recArr  = @(0.0)
    $denomP = $flEnv.TruePositives + $flEnv.FalsePositives
    $denomR = $flEnv.TruePositives + $flEnv.FalseNegatives
    if ($denomP -gt 0) { $precArr[0] = $flEnv.TruePositives; $precArr[0] /= $denomP }
    if ($denomR -gt 0) { $recArr[0]  = $flEnv.TruePositives; $recArr[0]  /= $denomR }
    $precPct = [Math]::Round($precArr[0] * 100, 1)
    $recPct  = [Math]::Round($recArr[0]  * 100, 1)

    Write-Host ""
    Write-Host "╔══════════════════════════════════════════════════╗" -ForegroundColor Cyan
    Write-Host "║ Phase 16: Federated Learning - Results ║" -ForegroundColor Cyan
    Write-Host "╠══════════════════════════════════════════════════╣" -ForegroundColor Cyan
    Write-Host ("║ Baseline (random) avg reward : {0,8} ║" -f $bAvg)        -ForegroundColor Gray
    Write-Host ("║ Trained (DQN) avg reward : {0,8} ║" -f $tAvg)        -ForegroundColor Green
    Write-Host ("║ Improvement : {0,7}% ║" -f $improvement) -ForegroundColor Yellow
    Write-Host "╠══════════════════════════════════════════════════╣" -ForegroundColor Cyan
    Write-Host ("║ Precision (Val+Rollback corr) : {0,7}% ║" -f $precPct)     -ForegroundColor Cyan
    Write-Host ("║ Recall (bad updates caught): {0,7}% ║" -f $recPct)      -ForegroundColor Cyan
    Write-Host "╠══════════════════════════════════════════════════╣" -ForegroundColor Cyan
    Write-Host "║ Agent learned to: ║" -ForegroundColor Cyan
    Write-Host "║ Collect gather updates from nodes ║" -ForegroundColor White
    Write-Host "║ Aggregate merge into global model ║" -ForegroundColor White
    Write-Host "║ Validate verify quality before deploy ║" -ForegroundColor White
    Write-Host "║ Rollback reject bad updates immediately ║" -ForegroundColor White
    Write-Host "╚══════════════════════════════════════════════════╝" -ForegroundColor Cyan
    Write-Host ""

    return @{ Agent = $agent; Results = $results; Baseline = @{ Avg = $bAvg }; Trained = @{ Avg = $tAvg } }
}

# ============================================================
# TEST SUGGESTIONS
# ============================================================
# 1. Run VBAF.LoadAll.ps1 (loads core DQN + all pillars)
#
# 2. QUICK DEMO (simulated federation conditions)
# $r = Invoke-VBAFFederatedLearningTraining -Episodes 100 -PrintEvery 10 -SimMode
#
# 3. FULL TRAINING (real jobs, network latency, CPU data)
# $r = Invoke-VBAFFederatedLearningTraining -Episodes 100 -PrintEvery 10
#
# 4. SKIP REAL DATA PROBE
# $r = Invoke-VBAFFederatedLearningTraining -Episodes 100 -PrintEvery 10 -SkipRealData
#
# 5. INSPECT AGENT DECISIONS
# $env = [FederatedLearningEnvironment]::new()
# $state = $env.Reset()
# Write-Host "Divergence: $($env.ModelDivergence) Quality: $($env.UpdateQuality)"
# $action = $r.Agent.Act($state)
# $labels = @("Collect","Aggregate","Validate","Rollback")
# Write-Host "Federation decision: $($labels[$action])"
#
# 6. VIEW CONFUSION MATRIX
# Write-Host "True Positives : $($env.TruePositives)"
# Write-Host "False Positives: $($env.FalsePositives)"
# Write-Host "True Negatives : $($env.TrueNegatives)"
# Write-Host "False Negatives: $($env.FalseNegatives)"
# ============================================================
Write-Host "📦 VBAF.Enterprise.FederatedLearning.ps1 loaded [v3.6.0 🔗]" -ForegroundColor Green
Write-Host " Phase 16: Federated Learning Intelligence"                   -ForegroundColor Cyan
Write-Host " Function : Invoke-VBAFFederatedLearningTraining"             -ForegroundColor Cyan
Write-Host ""
Write-Host " Quick start:" -ForegroundColor Yellow
Write-Host ' $r = Invoke-VBAFFederatedLearningTraining -Episodes 100 -PrintEvery 10 -SimMode' -ForegroundColor White
Write-Host ""